TP2_S39.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yci-EMhVx6d7"
      },
      "source": [
        "Obtenga un vector que indique si una compañía trabajo/no trabajo en una película, para cada película. Para cada compañía utilice estos vectores para buscar cuál es la compañía más similar, utilizando la distancia coseno. Imprima toda la lista de tuplas (compañía, similar)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxKiBw9D-OLV",
        "outputId": "e929c37a-abd3-410e-c6bf-3b7adc6e70bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyspark\n",
            "  Downloading pyspark-3.3.0.tar.gz (281.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 281.3 MB 38 kB/s \n",
            "\u001b[?25hCollecting py4j==0.10.9.5\n",
            "  Downloading py4j-0.10.9.5-py2.py3-none-any.whl (199 kB)\n",
            "\u001b[K     |████████████████████████████████| 199 kB 47.7 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.3.0-py2.py3-none-any.whl size=281764026 sha256=aa3330ed492f61c46c18eb12b7b1536816bf4c28f5ea4f5ae5f02073921e5049\n",
            "  Stored in directory: /root/.cache/pip/wheels/7a/8e/1b/f73a52650d2e5f337708d9f6a1750d451a7349a867f928b885\n",
            "Successfully built pyspark\n",
            "Installing collected packages: py4j, pyspark\n",
            "Successfully installed py4j-0.10.9.5 pyspark-3.3.0\n"
          ]
        }
      ],
      "source": [
        "#Realizamos las importanciones\n",
        "!pip install pyspark"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "YHU6_MhL-phX"
      },
      "outputs": [],
      "source": [
        "#Realizamos las importaciones para usar google drive y pyspark\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "from pyspark.sql import *\n",
        "from pyspark.sql.functions import *\n",
        "from pyspark import SparkContext\n",
        "from pyspark.sql import SQLContext\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DtAZO599-qXv"
      },
      "outputs": [],
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RmVXC0LQ-rLF"
      },
      "outputs": [],
      "source": [
        "#Descargamos movies.parquet\n",
        "descarga = drive.CreateFile({'id':'1YKh46_KdgSC-6UZ0mttbOqVI6tXgly-F'})\n",
        "descarga.GetContentFile('movies.parquet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SS8AgRpK-yDO"
      },
      "outputs": [],
      "source": [
        "spark = SparkSession.builder.getOrCreate() #Obtenemos una sesion de spark\n",
        "sc = spark.sparkContext #Obtenemos el contexto"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNVDhMoF-zSd"
      },
      "outputs": [],
      "source": [
        "#Creo los rdd con los que voy a trabajar\n",
        "sqlContext = SQLContext(sc)\n",
        "df = sqlContext.read.parquet('movies.parquet',header=True,inferSchema=True) \n",
        "moviesRdd = df.rdd "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hd-b644RhqSf"
      },
      "outputs": [],
      "source": [
        "moviesRdd.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9F5ZDCo0Zpsl",
        "outputId": "0f04a501-f6cb-4c81-d28c-23e1be1e9f5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "33585"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Filtro aquellas peliculas que no tengan asociada una compañia. Luego mapeo de forma que me quede \n",
        "moviesRdd = moviesRdd.filter(lambda x: x.production_companies != None)\n",
        "moviesRdd.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQcy4MGI206-",
        "outputId": "e9bab41a-4db8-4c6f-db12-f8e7819e3831"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('2550', {'6795', '8844'}),\n",
              " ('10201',\n",
              "  {'10029',\n",
              "   '10342',\n",
              "   '11082',\n",
              "   '11087',\n",
              "   '11112',\n",
              "   '11511',\n",
              "   '11630',\n",
              "   '12154',\n",
              "   '14052',\n",
              "   '15771',\n",
              "   '161495',\n",
              "   '16562',\n",
              "   '16889',\n",
              "   '1727',\n",
              "   '19124',\n",
              "   '20242',\n",
              "   '2054',\n",
              "   '2348',\n",
              "   '27281',\n",
              "   '2787',\n",
              "   '27969',\n",
              "   '32144',\n",
              "   '41374',\n",
              "   '423',\n",
              "   '43634',\n",
              "   '45096',\n",
              "   '46029',\n",
              "   '46094',\n",
              "   '47898',\n",
              "   '4806',\n",
              "   '68430',\n",
              "   '79593',\n",
              "   '85411',\n",
              "   '8844',\n",
              "   '9057',\n",
              "   '9092'})]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Lo primero que hago es realizar un flat map de forma tal que me quede cada registro de la forma (idCompania,idMovie) de forma tal que si una pelicula fue hecha por varias compañias quede separada.\n",
        "#Luego realizo un reduceByKey juntando para cada compañia todas las peliculas que hizo quedando (idCompania,listaIdMovie)\n",
        "#Por ultimo realizo un map para que esa lista de movies que tenemos se transforme en un set.\n",
        "moviesCompaniesRdd = moviesRdd.flatMap(lambda x: [(w,x.id) for w in x.production_companies.split(',')]).reduceByKey(lambda a,b: a+','+b)\\\n",
        ".map(lambda x: (x[0],set(x[1].split(','))))\n",
        "moviesCompaniesRdd.take(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVSYU8af3G0b",
        "outputId": "1ed28492-11b9-42d4-9fed-5b5a7c4c181d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "23692"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "moviesCompaniesRdd.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_sITs6RlHee"
      },
      "source": [
        "Vemos que en total tenemos 23692 companias diferentes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oiPX9r2V25BT"
      },
      "outputs": [],
      "source": [
        "#Lo que hago aca es hacer una especie de join consigo mismo con cartesian. Lo que hace es juntarme todas las combinaciones de registros de a pares.\n",
        "#Es decir si yo tengo los siguientes registros: regA,regB,regC lo que hace es generarme los siguientes registros: (regA,regA),(regA,regB),(regA,regC),(regB,regA),(regB,regB),(regB,regC),(regC,regA),(regC,regB),(regC,regC)\n",
        "#Con esto me ahorro hacer una doble iteracion en busca de ver que compañia es mas similar. \n",
        "moviesCompaniesSimilaresRdd = moviesCompaniesRdd.cartesian(moviesCompaniesRdd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9_v1vIJ9UC6",
        "outputId": "375b89da-520f-47cb-f437-9f4d407b6d33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "561310864"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "moviesCompaniesSimilaresRdd.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nl4IvfdFmrEJ"
      },
      "source": [
        "Ahora tengo 561310864 registros con todas las combinaciones posibles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIfCVFiBPNB6",
        "outputId": "34f1d1d9-5a0b-4874-e52e-912a05a2e1ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(('2550', {'6795', '8844'}), ('2550', {'6795', '8844'}))"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Vemos como quedarian los registros formados. La forma es la siguiente ((idCompania,{idsMovies}),((idCompania,{idsMovies}))) donde las movies que acompañan a las companias son las movies que esas compañias producieron\n",
        "moviesCompaniesSimilaresRdd.first()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bBR5zmeadqVQ"
      },
      "outputs": [],
      "source": [
        "#Aca filtro el primer registro de cada compañia ya que es consigo mismo que se compara y no lo queremos asi que filtro aquellas que tengan el mismo idCompania.\n",
        "#Luego realizo un map de forma tal que mis registros queden de la forma: (idCompania1,(moviesIdCompania1,(idCompania2,moviesIdCompania2)))\n",
        "moviesCompaniesSimilaresRdd = moviesCompaniesSimilaresRdd.filter(lambda x: x[0][0]!=x[1][0]).map(lambda x: (x[0][0],(x[0][1],x[1])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fPb5Ol5sVmn",
        "outputId": "3d940f25-1ca4-454c-f6d1-5c8074dc9d80"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('2550',\n",
              " ({'6795', '8844'},\n",
              "  ('10201',\n",
              "   {'10029',\n",
              "    '10342',\n",
              "    '11082',\n",
              "    '11087',\n",
              "    '11112',\n",
              "    '11511',\n",
              "    '11630',\n",
              "    '12154',\n",
              "    '14052',\n",
              "    '15771',\n",
              "    '161495',\n",
              "    '16562',\n",
              "    '16889',\n",
              "    '1727',\n",
              "    '19124',\n",
              "    '20242',\n",
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              "    '79593',\n",
              "    '85411',\n",
              "    '8844',\n",
              "    '9057',\n",
              "    '9092'})))"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "moviesCompaniesSimilaresRdd.first()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "L0AA4BqS-HYR",
        "outputId": "d35b1bc3-0a1d-4e23-b93e-8a9e830e6b69"
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              " ('7024', '12600'),\n",
              " ('13192', '11037'),\n",
              " ('14188', '44731'),\n",
              " ('11333', '92940'),\n",
              " ('94200', '94199'),\n",
              " ('94201', '94199'),\n",
              " ('56519', '553'),\n",
              " ('20858', '88753'),\n",
              " ('55642', '55643'),\n",
              " ('55643', '55642'),\n",
              " ('11480', '16704'),\n",
              " ('7331', '42652'),\n",
              " ('690', '689'),\n",
              " ('12626', '95593'),\n",
              " ('304', '303'),\n",
              " ...]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import math #Importo la libreria math para utilizar la raiz cuadrada\n",
        "\n",
        "#Por ultimo lo que hago ahora es buscar las distancias coseno mas grandes para cada compañia y asi obtener la mas similar.\n",
        "#Lo que hago entonces es hacer un reduceByKey donde vamos a operarar por cada registro haciendo la distancia coseno y quedandome con la mayor.\n",
        "moviesCompaniesSimilaresRdd.reduceByKey(lambda a,b: a if((len(a[1][1].intersection(a[0])))/(math.sqrt(len(a[1][1]))*math.sqrt(len(a[0])))>(len(b[1][1].intersection(b[0])))/(math.sqrt(len(b[1][1]))*math.sqrt(len(b[0])))) else b).map(lambda x: (x[0],x[1][1][0])).collect()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}