entrega3.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "entrega3.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Entrega 3 código - Proyecto IIC2433 Minería de Datos**\n",
        "\n",
        "**Grupo: 22**\n",
        "\n",
        "Integrantes:\n",
        "\n",
        "* Daniela Corral: 18637019\n",
        "* Matías Masjuan: 18639399\n",
        "* Belén Silva: 18627390\n",
        "* Benjamín Vicente: 19637381"
      ],
      "metadata": {
        "id": "Fw3sEJ3sgXix"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0) Instalación de dependencias"
      ],
      "metadata": {
        "id": "pdrHhZ1kgaqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Esto puede ser ignorado si existen los archivos text.npy y text-title.npy\n",
        "!python -m spacy download en_core_web_lg \n",
        "!pip install diskcache"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2VyEfTnhIL7",
        "outputId": "9d4b7c57-3391-40c0-d118-f9f7a2fbb904"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting en_core_web_lg==2.2.5\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.2.5/en_core_web_lg-2.2.5.tar.gz (827.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 827.9 MB 1.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_lg==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (4.62.3)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.6)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.8.2)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.0.6)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.6)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (4.8.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.10.0.2)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2021.10.8)\n",
            "Building wheels for collected packages: en-core-web-lg\n",
            "  Building wheel for en-core-web-lg (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for en-core-web-lg: filename=en_core_web_lg-2.2.5-py3-none-any.whl size=829180942 sha256=c8697a54342f6a9a360dbf77c19b254cbb2e4fc924dce9c8cf3379521c5fff78\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-n7k_jkuo/wheels/11/95/ba/2c36cc368c0bd339b44a791c2c1881a1fb714b78c29a4cb8f5\n",
            "Successfully built en-core-web-lg\n",
            "Installing collected packages: en-core-web-lg\n",
            "Successfully installed en-core-web-lg-2.2.5\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_lg')\n",
            "Collecting diskcache\n",
            "  Downloading diskcache-5.3.0-py3-none-any.whl (44 kB)\n",
            "\u001b[K     |████████████████████████████████| 44 kB 2.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: diskcache\n",
            "Successfully installed diskcache-5.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy validate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dude_-WghrhL",
        "outputId": "70b9fc0f-6cf2-42fa-aaaa-e6677ed7f574"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r⠙ Loading compatibility table...\r\u001b[2K\u001b[38;5;2m✔ Loaded compatibility table\u001b[0m\n",
            "\u001b[1m\n",
            "====================== Installed models (spaCy v2.2.4) ======================\u001b[0m\n",
            "\u001b[38;5;4mℹ spaCy installation: /usr/local/lib/python3.7/dist-packages/spacy\u001b[0m\n",
            "\n",
            "TYPE      NAME             MODEL            VERSION                            \n",
            "package   en-core-web-sm   en_core_web_sm   \u001b[38;5;2m2.2.5\u001b[0m   \u001b[38;5;2m✔\u001b[0m\n",
            "package   en-core-web-lg   en_core_web_lg   \u001b[38;5;2m2.2.5\u001b[0m   \u001b[38;5;2m✔\u001b[0m\n",
            "link      en               en_core_web_sm   \u001b[38;5;2m2.2.5\u001b[0m   \u001b[38;5;2m✔\u001b[0m\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "import sys\n",
        "\n",
        "if 'google.colab' in sys.modules:\n",
        "    from google.colab import drive\n",
        "    drive.mount('/gdrive')\n",
        "\n",
        "    if Path(\"/gdrive/Shareddrives/Proyecto Minería de Datos\").exists():\n",
        "        !cp \"/gdrive/Shareddrives/Proyecto Minería de Datos/textv2.npy\" \"text.npy\"\n",
        "        !cp \"/gdrive/Shareddrives/Proyecto Minería de Datos/text-title.npy\" \"text-title.npy\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8N0PdA7lh7ia",
        "outputId": "88be68d8-bfa1-4765-f326-3933605e405b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1) Definición del problema a analizar: *Fake news*"
      ],
      "metadata": {
        "id": "hd2AMFXpii_c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las *fake news* (en español, noticias falsas) son un término referido a la divulgación de noticias falsas que provocan un peligroso círculo de desinformación. En el siglo XXI, las redes sociales y el internet han permitido que cualquier persona pueda ser creadora de contenido para la web, lo que ha facilitado la viralización de contenido engañoso, falso o fabricado. En efecto, un estudio de la revista Science (2018) demostró que las *fake news* son retuiteadas por hasta 100 veces más personas y mucho más rápido que la información genuina, especialmente al hablar de política ([Revista Science](https://www.science.org/doi/10.1126/science.aap9559)). Así, se generan círculos viciosos y una noticia falsa se puede replicar miles de veces en solo segundos. (FIP, p.1)\n",
        "\n",
        "Queremos analizar el problema de detección de *fake news*. Para esto, usaremos los datos del ISOT Fake News Dataset, el cual está compuesto por dos archivos csv: “True.csv”, que contiene artículos de noticias reales recolectados desde el sitio de noticias *Reuters.com*; y “Fake.csv” que contiene artículos recolectados desde sitios poco confiables verificados por la organización estadounidense de fact-checking Politifact. \n",
        "\n",
        "**El plan es procesar las noticias con herramientas de procesamiento de lenguaje natural, y clasificar noticias como verdaderas o falsas de acuerdo a su contenido utilizando clasificadores como Naive Bayes y Decision Trees.**"
      ],
      "metadata": {
        "id": "ctNXxcNMirxm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2) Impacto del problema"
      ],
      "metadata": {
        "id": "0aIhA-ioi5n8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La circulación de fake news no sólo desinforma a las personas impactando su derecho a la información, sino que además afecta a la sociedad en su conjunto y a la integridad democrática. Casos conocidos de fake news usualmente involucran elecciones presidenciales o decisiones políticas que afectan a países completos o, en el caso de las elecciones presidenciales de EEUU, al régimen mundial.\n",
        "\n",
        "\n",
        "Por otro lado, la difusión de información falsa puede provocar como consecuencia aumentar la hostilidad y odio en contra de ciertos grupos vulnerables de la sociedad (FIP, p.11). Por ejemplo, si se desean ganar votos anti-inmigración, suelen publicarse noticias que adjudican aumento de crímenes o enfermedades de la mano al aumento de migrantes. \n",
        "\n",
        "\n",
        "Finalmente, las fake news no solo buscan desinformar para influenciar grandes eventos, sino que están presentes en miles de sitios web diariamente con el objetivo de generar dinero. Para esto, las personas utilizan engaños relacionados a problemas o acontecimientos que preocupan a grandes masas de personas, estando dispuestos a incluso generar pánico colectivo con tal de ganar más clicks y likes. \n",
        "\n",
        "\n",
        "Como grupo, opinamos que es innegable la enorme influencia que han ejercido las fake news hasta el momento, y todavía se desconoce el potencial impacto que podrían ser capaces de generar en eventos futuros. Creemos que es parte de nuestro rol como ingenieros en computación poner nuestro conocimiento al servicio de la comunidad (en este caso de los internautas) para poder brindar soluciones a problemas de ingeniería presentes en la vida real como éste y cooperar con nuestro grano de arena a hacer de internet y las RRSS mejores cada día. Por ello creemos que este proyecto será de alto impacto y muy contingente. \n"
      ],
      "metadata": {
        "id": "MbNMuApGi9DE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fuentes partes 1 y 2:**\n",
        "* ¿Qué son las Fake News?: guía para combatir la desinformación en la era de la posverdad | FIP. (2018). Recuperado de https://www.ifj.org/fileadmin/user_upload/Fake_News_-_FIP_AmLat.pdf\n",
        "* ¿Qué son las Fake News? | UNAM Global. (2020, 31 de Mayo). Recuperado de https://unamglobal.unam.mx/que-son-las-fake-news\n",
        "* ¿Por qué decimos cosas que sabemos que no son verdad? | Chequeado. (2020, 16 de Julio). Recuperado de https://chequeado.com/el-explicador/por-que-decimos-cosas-que-sabemos-que-no-son-verdad"
      ],
      "metadata": {
        "id": "SvfODXuZjAwY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3) Datos: limpieza y preparación"
      ],
      "metadata": {
        "id": "tt1kw2gajDu6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lo primero será importar las librerías necesarias para la realización de este proyecto."
      ],
      "metadata": {
        "id": "ed_Pq3kljF_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime\n",
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from pathlib import Path"
      ],
      "metadata": {
        "id": "2k2UOVvsiCj6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Obtención de datos\n",
        "\n",
        "A continuación, se cargarán los set de datos desde un link compartido en Dropbox y se verá una muestra de cada uno de ellos."
      ],
      "metadata": {
        "id": "i3zXlAzTjPcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O True.csv https://www.dropbox.com/s/tc43g22n8mm6718/True.csv?dl=0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBb2CMDbjQBs",
        "outputId": "aa748db0-5912-446b-c183-a3a21d25a9a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-18 21:06:07--  https://www.dropbox.com/s/tc43g22n8mm6718/True.csv?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/tc43g22n8mm6718/True.csv [following]\n",
            "--2021-12-18 21:06:07--  https://www.dropbox.com/s/raw/tc43g22n8mm6718/True.csv\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ucdbabb94e1129d32a741d901666.dl.dropboxusercontent.com/cd/0/inline/BcGDLYgolHR96Az-o0z46CV493U1oMJcI16JZEbuQTrYOBG_KzUd4LOGtdEdPoK9rroHxfmat3gTtApH2kPKawhBj2zg_hqI-NDIogObem6lHyI8VLeK2TfksdsU94N0qwkBoTz64OwqUNYlyUTjggSQ/file# [following]\n",
            "--2021-12-18 21:06:08--  https://ucdbabb94e1129d32a741d901666.dl.dropboxusercontent.com/cd/0/inline/BcGDLYgolHR96Az-o0z46CV493U1oMJcI16JZEbuQTrYOBG_KzUd4LOGtdEdPoK9rroHxfmat3gTtApH2kPKawhBj2zg_hqI-NDIogObem6lHyI8VLeK2TfksdsU94N0qwkBoTz64OwqUNYlyUTjggSQ/file\n",
            "Resolving ucdbabb94e1129d32a741d901666.dl.dropboxusercontent.com (ucdbabb94e1129d32a741d901666.dl.dropboxusercontent.com)... 162.125.2.15, 2620:100:601d:15::a27d:50f\n",
            "Connecting to ucdbabb94e1129d32a741d901666.dl.dropboxusercontent.com (ucdbabb94e1129d32a741d901666.dl.dropboxusercontent.com)|162.125.2.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 53582940 (51M) [text/plain]\n",
            "Saving to: ‘True.csv’\n",
            "\n",
            "True.csv            100%[===================>]  51.10M  30.8MB/s    in 1.7s    \n",
            "\n",
            "2021-12-18 21:06:10 (30.8 MB/s) - ‘True.csv’ saved [53582940/53582940]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O Fake.csv https://www.dropbox.com/s/yqnpv3nr0gemy5a/Fake.csv?dl=0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tdT1GwlmWy-",
        "outputId": "a5b2c4ad-76f6-4dd5-9570-b5ce8fcc4049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-18 21:06:10--  https://www.dropbox.com/s/yqnpv3nr0gemy5a/Fake.csv?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/yqnpv3nr0gemy5a/Fake.csv [following]\n",
            "--2021-12-18 21:06:10--  https://www.dropbox.com/s/raw/yqnpv3nr0gemy5a/Fake.csv\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc46f0523888c32ed899f12682e6.dl.dropboxusercontent.com/cd/0/inline/BcEQJ8W3r6HuxuLW-WxtcujlatBYfEjsbIQI2z3Fr8Ly1iyjUgWx0G6m3bgjIS9EkA382DowmpUFsWV07DoqyNFeATWTkmF_dBN1x43FUY6gF_ejMpr_7UBTPyRCqq60ORhOIrkpUyeGJtgIL5g2Xkg-/file# [following]\n",
            "--2021-12-18 21:06:11--  https://uc46f0523888c32ed899f12682e6.dl.dropboxusercontent.com/cd/0/inline/BcEQJ8W3r6HuxuLW-WxtcujlatBYfEjsbIQI2z3Fr8Ly1iyjUgWx0G6m3bgjIS9EkA382DowmpUFsWV07DoqyNFeATWTkmF_dBN1x43FUY6gF_ejMpr_7UBTPyRCqq60ORhOIrkpUyeGJtgIL5g2Xkg-/file\n",
            "Resolving uc46f0523888c32ed899f12682e6.dl.dropboxusercontent.com (uc46f0523888c32ed899f12682e6.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n",
            "Connecting to uc46f0523888c32ed899f12682e6.dl.dropboxusercontent.com (uc46f0523888c32ed899f12682e6.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 62789876 (60M) [text/plain]\n",
            "Saving to: ‘Fake.csv’\n",
            "\n",
            "Fake.csv            100%[===================>]  59.88M  51.1MB/s    in 1.2s    \n",
            "\n",
            "2021-12-18 21:06:12 (51.1 MB/s) - ‘Fake.csv’ saved [62789876/62789876]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_falsos = pd.read_csv(\"Fake.csv\")\n",
        "df_verdaderos = pd.read_csv(\"True.csv\")"
      ],
      "metadata": {
        "id": "t8iJX2vIme9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
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              "                                               title  ...               date\n",
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              "1   Drunk Bragging Trump Staffer Started Russian ...  ...  December 31, 2017\n",
              "2   Sheriff David Clarke Becomes An Internet Joke...  ...  December 30, 2017\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "                                               title  ...                date\n",
              "0  As U.S. budget fight looms, Republicans flip t...  ...  December 31, 2017 \n",
              "1  U.S. military to accept transgender recruits o...  ...  December 29, 2017 \n",
              "2  Senior U.S. Republican senator: 'Let Mr. Muell...  ...  December 31, 2017 \n",
              "3  FBI Russia probe helped by Australian diplomat...  ...  December 30, 2017 \n",
              "4  Trump wants Postal Service to charge 'much mor...  ...  December 29, 2017 \n",
              "\n",
              "[5 rows x 4 columns]"
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A partir de lo anterior, observamos que ambos datasets poseen los mismos atributos/features:\n",
        "\n",
        "* ```title```: Corresponde al título de la publicación o artículo. Es de tipo ***string*** (más bien ***text*** dada su extensión)\n",
        "* ```text```: Corresponde al cuerpo de la publicación o artículo. Es de tipo string (más bien ***text*** dada su extensión)\n",
        "* ```subject```: Corresponde al tema del que habla la publicación o artículo. Es de tipo ***string***\n",
        "* ```date```: Corresponde a la fecha de publicación. Es de tipo ***date***\n"
      ],
      "metadata": {
        "id": "C6s2qoLxopVC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**¿Son estos features independientes entre si?** Estos atributos claramente son independientes. Digamos que cada uno puede existir individualmente sin depender de otro atributo. Sin embargo, recordemos que cada uno de estos atributos son características de una noticia, por lo que evidentemente tienen cierta relación. Por ejemplo, el título puede influenciar en lo que dice en el texto, y también se relaciona con la temática en un sentido **semántico**, es decir, en el significado humano y cognitivo que se le da al texto. Sin embargo, no están relacionados en términos **sintácticos** (que es lo que lee la máquina), por lo que es posible establecer una independencia entre estos atributos."
      ],
      "metadata": {
        "id": "0M_oKKNEoqlS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Respecto a los valores de los features, se observa que los títulos y el texto corresponde a un dato propio de cada entidad. Por otro lado, la columna **subject**, la cual nos da cuenta de la temática de la noticia, tiene una cantidad limitada de categorías.\n",
        "\n",
        "Para el dataset ```df_falsos``` existen 6 categorías para este atributo: \\[ *News, politics, left-news, Government News, US_news, Middle-east* \\]"
      ],
      "metadata": {
        "id": "at6zcxFRozmE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_falsos['subject'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9fxOsSwoqHI",
        "outputId": "79b40ea3-f65f-4ce2-91c9-72a0c1d6d747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "News               9050\n",
              "politics           6841\n",
              "left-news          4459\n",
              "Government News    1570\n",
              "US_News             783\n",
              "Middle-east         778\n",
              "Name: subject, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por otra parte, para el dataset ```df_verdaderos``` existen solo 2:\n",
        "\\[ *politicsNews, worldnews* \\]"
      ],
      "metadata": {
        "id": "qikL9b_npCIU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_verdaderos['subject'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ok6wpfsXpFL2",
        "outputId": "af5e3eb8-39ae-4507-fa3b-2a4b52afd1f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "politicsNews    11272\n",
              "worldnews       10145\n",
              "Name: subject, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si queremos hacer un join de estos dataset es necesario tener en cuenta este atributo para relacionar estos sets categóricos distintos. Por ejemplo, *politics* y p*oliticsNews* hacen referencia a publicaciones políticas, y no tendría sentido tratarlos como categorías diferentes.\n",
        "\n",
        "Otro de los problemas que tienen estos datasets es el tipo de dato o *datatype* de las columnas. No podemos simplemente manejar los textos en su forma completa, sino que es necesario aplicar algún tipo de algoritmo para vectorizar la información entregada y que sea posible entrenar los modelos que tenemos con ella.\n",
        "\n",
        "Existen otro tipo de consideraciones que es necesario tener en cuenta, como el uso de *stopwords*, las cuales son palabras que se usan en todas las oraciones pero que no tienen una relación directa con la etiqueta de clasificación (noticia falsa o verdadera). **De esto se explicará en mayor detalle más adelante**."
      ],
      "metadata": {
        "id": "rkeMFkzbpOf7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Limpieza de datos"
      ],
      "metadata": {
        "id": "-diyDa0Tp6FS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En esta sección se limpiarán los datos y se añadirán/quitarán columnas para beneficiar el mecanismo de procesamiento de la información.\n",
        "\n",
        "A continuación se crea la función `cast_date`, que transforma el formato de la fecha para darle un tipo de dato *date*, en lugar de que solo sea un texto con la fecha escrita. De este modo nos será más fácil aplicar operaciones numéricas."
      ],
      "metadata": {
        "id": "MHErxSh8qB-i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cast_date(obj: str, *, fs: \"list[str]\" = [\"%B %d, %Y\", \"%d-%b-%y\", \"%b %d, %Y\"]) -> datetime.date:\n",
        "    for f in fs:\n",
        "        try:\n",
        "            return datetime.datetime.strptime(obj.strip(), f)\n",
        "        except ValueError: continue\n",
        "    warnings.warn(f\"{repr(obj)} is not a date\")\n",
        "    return None"
      ],
      "metadata": {
        "id": "RpujakcTpmDS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "También se crea la función `unify_politic_news` que unificará las categorías 'politicsNews' y 'politics' como una única categoría 'politicsNews'. Si bien para la entrega pasada de este proyecto se utilizaron solamente noticias de tema político, para esta entrega se propuso utilizar la totalidad de las noticias. Por ende, la columna `subject` será importante."
      ],
      "metadata": {
        "id": "cfXKfn5ew_Yq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def unify_politic_news(category):\n",
        "    if category == 'politics':\n",
        "        return 'politicsNews'\n",
        "    return category"
      ],
      "metadata": {
        "id": "6kPE4yeYrsTU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación se genera el *dataset* `df_all`, el cual agrupa los dos dataset anteriores en uno solo. Además, aplica la función de transformación de fecha y se hace shuffle de sus datos."
      ],
      "metadata": {
        "id": "DTZZiik0qP7u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Se generan dataframes temporales\n",
        "df_falsos_temp = df_falsos.copy()\n",
        "df_verdaderos_temp = df_verdaderos.copy()\n",
        "\n",
        "#Agregamos nueva columna is_fake\n",
        "df_falsos_temp['is_fake'] = True\n",
        "df_verdaderos_temp['is_fake'] = False\n",
        "\n",
        "#Juntamos datasets de noticias reales con las falsas\n",
        "df_all = pd.concat([df_falsos_temp, df_verdaderos_temp])\n",
        "df_all['date'] = df_all['date'].apply(cast_date)\n",
        "df_all['subject'] = df_all['subject'].apply(unify_politic_news)\n",
        "\n",
        "df_all = df_all.sample(frac=1, random_state=3).reset_index(drop=True)\n",
        "\n",
        "#Borramos datasets temporales\n",
        "del df_falsos_temp, df_verdaderos_temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygXrvYacqKL1",
        "outputId": "884686cb-cfb2-4af5-d499-9553485a1ea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: 'https://100percentfedup.com/served-roy-moore-vietnamletter-veteran-sets-record-straight-honorable-decent-respectable-patriotic-commander-soldier/' is not a date\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: 'https://100percentfedup.com/video-hillary-asked-about-trump-i-just-want-to-eat-some-pie/' is not a date\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: 'https://100percentfedup.com/12-yr-old-black-conservative-whose-video-to-obama-went-viral-do-you-really-love-america-receives-death-threats-from-left/' is not a date\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: 'https://fedup.wpengine.com/wp-content/uploads/2015/04/hillarystreetart.jpg' is not a date\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: 'https://fedup.wpengine.com/wp-content/uploads/2015/04/entitled.jpg' is not a date\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: 'MSNBC HOST Rudely Assumes Steel Worker Would Never Let His Son Follow in His Footsteps…He Couldn’t Be More Wrong [Video]' is not a date\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n80Iui6UADFb",
        "outputId": "f97850fb-5659-4119-b12e-16695999f43b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44898"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Datos nulos\n",
        "Una parte importantísima de la limpieza de cualquier base de datos es limpiar filas con datos nulos. A continuación se hace dicha limpieza."
      ],
      "metadata": {
        "id": "ewV-ckRvw42z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_all = df_all.dropna()"
      ],
      "metadata": {
        "id": "sUHqa7FKvjhQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdeMHtX3zpgq",
        "outputId": "e4407af9-b29e-431b-fbf2-4118440c1911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44888"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Datos duplicados\n",
        "\n",
        "A continuación se observan los datos y se busca información duplicada."
      ],
      "metadata": {
        "id": "Gmu_d-aLy93v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Borramos filas completamente duplicadas (si hubieran)\n",
        "df_all = df_all.drop_duplicates()"
      ],
      "metadata": {
        "id": "kReeupVHzBOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoG8OVT6zwme",
        "outputId": "ed39cd23-a3bf-4317-8ab7-429e789763c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44679"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Al ejecutar el comando anterior, se deberían haber borrado del dataset todo dato tal que el valor de cada una de sus features es igual al de otro dato al mismo tiempo. Sin embargo, es dificil que una situación así pueda ocurrir. En muchos casos, existe la posibilidad de que solo el título se encuentre duplicado en muchas filas, o que el texto se encuentre duplicado, pero que los otros atributos también sean distintos. Dado que trabajaremos solo con el texto y título de las noticias, nos interesa que esos datos sean los que no estén repetidos en varias noticias."
      ],
      "metadata": {
        "id": "F64zBchC0yR-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_all = df_all.drop_duplicates(subset=['title','text'])\n",
        "df_all.reset_index(drop=True, inplace=True)"
      ],
      "metadata": {
        "id": "Mt7_oyX0zDrz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iq-UVLsFz2DR",
        "outputId": "b7078740-70fa-4815-b0f9-c2c1fbfce11c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39099"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finalmente, observamos estadísticas de nuestro dataset completamente limpio."
      ],
      "metadata": {
        "id": "aBKNYnmw0JBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_all.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "ue8ZZDVW0Jdw",
        "outputId": "fc2a4d70-1813-4501-9b1f-b7bd02d793eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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              "                                                    title  ... is_fake\n",
              "count                                               39099  ...   39099\n",
              "unique                                              38723  ...       2\n",
              "top     Factbox: Trump fills top jobs for his administ...  ...   False\n",
              "freq                                                   14  ...   21197\n",
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              "\n",
              "[6 rows x 5 columns]"
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        "La proporción de noticias falsas vs verdaderas en los datos es la siguiente:"
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        "id": "gY9iSPOR0dl9"
      }
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          "data": {
            "text/plain": [
              "False    21197\n",
              "True     17902\n",
              "Name: is_fake, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "plt.bar(['Real news', 'Fake news'], df_all['is_fake'].value_counts())"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
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        "id": "EpYFLXRh_P2Q",
        "outputId": "b77eb48a-9cfc-4108-dd86-9c82205dbf32"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 2 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
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          "data": {
            "image/png": 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MO510J3BekrntwvF5rSZJmgH9niaaB9yeZHRdn6uqryfZDNya5HLgceDi1n4DcCEwDDwHvBugqvYm+RiwubX7aFXt7bNvkqQJ6isMquoR4PVj1J8Czh2jXsAV46xrLbC2n/5IkibHbyBLkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJPoIgySLknwryQNJ7k/ygVa/OsnOJFvacGFnmY8kGU7yUJLzO/XlrTacZHV/b0mSdLj6ebjNfuDDVfW99ujLe5JsbPM+VVUf7zZOcjpwCfA64OeAbyZ5TZv9aeBtwA5gc5L1VfVAH32TJB2GSYdBe3bxrjb+t0keBBYcZJEVwC1VtQ94NMkwcFabN9yemkaSW1pbw0CSZsiUXDNIshh4A/CdVroyydYka9sD7qEXFE90FtvRauPVx9rOqiRDSYZGRkamouuSJKYgDJK8DLgN+GBVPQtcD7waWEbvyOET/W5jVFWtqarBqhocGBiYqtVK0jGvn2sGJDmeXhB8tqq+BFBVuzvzPwN8pU3uBBZ1Fl/YahykLkmaAf3cTRTgBuDBqvpkpz6/0+wdwLY2vh64JMmJSZYAS4HvApuBpUmWJDmB3kXm9ZPtlyTp8PVzZPArwDuB+5JsabXfAy5Nsgwo4DHgvQBVdX+SW+ldGN4PXFFVzwMkuRK4E5gDrK2q+/volyTpMPVzN9GfAxlj1oaDLHMNcM0Y9Q0HW06SNL38BrIkyTCQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJHEEhUGS5UkeSjKcZPVs90eSjiVHRBgkmQN8GrgAOJ3eozNPn91eSdKx44gIA+AsYLiqHqmqHwG3ACtmuU+SdMyY9DOQp9gC4InO9A7g7AMbJVkFrGqTP0jy0Az07VhwGvDXs92JI0F+f7Z7oHG4jzZTsI/+wljFIyUMJqSq1gBrZrsff98kGaqqwdnuhzQe99Hpd6ScJtoJLOpML2w1SdIMOFLCYDOwNMmSJCcAlwDrZ7lPknTMOCJOE1XV/iRXAncCc4C1VXX/LHfrWOKpNx3p3EenWapqtvsgSZplR8ppIknSLDIMJEmGwZEuyfNJtiTZluTLSU6Z5HreleQPprp/UmcfHR0Wj9NucZJtM9s7TZRhcOT7YVUtq6ozgL3AFbPdIekAo/vo6PDYbHdIh88wOLr8Fb1va5Pk1Um+nuSeJN9O8g9b/V8k+U6Se5N8M8m8g60wydVJ1ib50ySPJPndzrx/neS77dPe/0wyJ8lvJflkm/+BJI+08Vcl+Ys2fm2SB5JsTfLxafqz0BEqycuSbEryvST3JXnJT8u0/eXeJP94vH35gPbup9OtqhyO4AH4QXudA3wBWN6mNwFL2/jZwJ+08bm8cJfYvwE+0cbfBfzBGOu/GvhL4ER6X/l/Cjge+CXgy8Dxrd0fApcB/wDY3GpfpPcdkQXASuC/Aq8EHur04ZTZ/jN0mPZ99HlgSxtup3fL+slt3mnAMBBgMbANeC1wL/D61mbMffmAbbifTvNwRHzPQAd1UpIt9HbkB4GNSV4G/FPgC0lG253YXhcCn08yHzgBeHQC2/hqVe0D9iXZA8wDzgXOBDa3bZwE7KmqJ9snv5fT+9b454A3A28CvgQ8A/xf4IYkXwG+0te719Hgh1W1bHQiyfHAf0nyZuAn9Pbd0SPUAeAO4Deq6oFD7MsHcj+dRp4mOvKN/kP7BXqfrq6g9/f2N/Xi87S/1Nr/D3pHAP8IeC/w0xPYxr7O+PP0PtkFWNdZ/2ur6urW5i+Bd9P7ZPVtev/A/gnwF1W1n96v0H4ReDvw9cm+cR21fpvef/pntn13Ny/sh88A/wf41TZ9sH35QO6n08gwOEpU1XPA7wIfBp4DHk3yWwDpeX1r+gpe+F2nlX1schPwm0l+tm3j1CSjv3b4beDfAXfRO9x/K7Cvqp5pn/ReUVUbgH8LvP6lq9bfc6+g9+n8x0neyot/JfNHwDuAy5L8q6p6lvH35YlwP50iniY6ilTVvUm2ApfS+/R1fZL/RO/c6S3A/6J3bvULSZ4G/gRYMsltPdDW/Y0kPwX8mN5RyeP0/pEtAu6qqueTPAF8vy36cuCOJD9N71Pbhyb1ZnU0+yzw5ST3AUO8sG8AUFV/l+Tt9E55/oDx9+VDcj+dOv4chSTJ00SSJMNAkoRhIEnCMJAkYRhIkjAMJEkYBpIk4P8BRGI3hYbvHkUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Y la cantidad de noticias por categoría se presenta a continuación:"
      ],
      "metadata": {
        "id": "42lIl9Ts0kFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_all['subject'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pn_OquRa0g6e",
        "outputId": "c1ff357e-2e82-46bd-957f-a9480d1baffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "politicsNews       15881\n",
              "worldnews           9986\n",
              "News                9050\n",
              "left-news           2484\n",
              "Government News      915\n",
              "US_News              397\n",
              "Middle-east          386\n",
              "Name: subject, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "df_all['subject'].value_counts().plot.bar(ax=ax)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), fontsize=12, rotation=45, ha=\"right\", rotation_mode='anchor')\n",
        "fig.savefig(\"dist-noticias.pdf\", bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "-66OeH6ZkeQi",
        "outputId": "0f493458-ca28-4061-ca09-a17a29c68ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Procesamiento de las noticias con herramientas de PLN"
      ],
      "metadata": {
        "id": "1MsmbBtk1bLK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizaremos la librería `spaCy`, un ***natural language toolkit***, para aplicar procesamiento del lenguaje natural que nos permita extraer la mayor cantidad de información de nuestras noticias."
      ],
      "metadata": {
        "id": "YnLPNdmD10Ah"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Esto puede ser ignorado si existen los archivos text.npy y text-title.npy\n",
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_lg\")"
      ],
      "metadata": {
        "id": "hMOzcbnf2CoC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "La función `process_text` nos ayuda a aplicar *word embeddings* de Spacy. En esta técnica se pide directamente a Spacy el vector que representa a cada frase recibida. Además, utilizamos un sistema de caché para que en caso de que desee calcular el vector de alguna frase que ya fue procesada, se entregue inmediatamente ese resultado."
      ],
      "metadata": {
        "id": "Bdfhc6O-3oUN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Esto puede ser ignorado si existen los archivos text.npy y text-title.npy\n",
        "import functools\n",
        "import diskcache\n",
        "\n",
        "process_text_cache = diskcache.Cache(\n",
        "    directory=\"/gdrive/Shareddrives/Proyecto Minería de Datos/process_text_cache_v2/\"\n",
        ")\n",
        "\n",
        "@process_text_cache.memoize()\n",
        "def process_text(text):\n",
        "    return nlp(text).vector"
      ],
      "metadata": {
        "id": "phIdovZA3scL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Una vez transformado el texto eliminando las stop words junto con las transformaciones de palabras por sus respectivos lemas, entre otros procesamiento del lenguaje natural, se guarda un nuevo dataframe con los textos procesados con la finalidad de tener rápido acceso al dataset con el que trabajaremos de aquí en adelante y no necesitar correr el código con PLN otra vez."
      ],
      "metadata": {
        "id": "cQ5IZ0Pg4AuX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Si existe un archivo text.npy (con los textos procesados), lo usamos y evitamos la hora de \n",
        "#procesamiento con PLN\n",
        "saved_text_path = Path('text.npy')\n",
        "if saved_text_path.exists():\n",
        "    with saved_text_path.open(\"rb\") as f:\n",
        "        text = np.load(f)\n",
        "\n",
        "# En caso contrario, procesamos los datos y los guardamos en un archivo para no volver a correrlo\n",
        "else:\n",
        "    text = np.vectorize(process_text, signature=\"()->(k)\")(df_all['text'].to_numpy())  # 57 minutos\n",
        "    with saved_text_path.open(\"wb\") as f:\n",
        "        np.save(f, text)"
      ],
      "metadata": {
        "id": "d_hdiPQk3ls4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Idem que la celda superior, pero en este caso con los textos y títulos de las noticias\n",
        "saved_text_path = Path('text-title.npy')\n",
        "\n",
        "if saved_text_path.exists():\n",
        "    with saved_text_path.open(\"rb\") as f:\n",
        "        textPlusTitle = np.load(f)\n",
        "else: \n",
        "    df_textPlusTitle = df_all['title'] + df_all['text']\n",
        "    textPlusTitle = np.vectorize(process_text, signature=\"()->(k)\")(df_textPlusTitle.to_numpy())  # ?? minutos\n",
        "    with saved_text_path.open(\"wb\") as f:\n",
        "        np.save(f, textPlusTitle)"
      ],
      "metadata": {
        "id": "ISjCXOem6hJh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Vectorizador y separación de Xtrain e Xtest\n",
        "Ahora que tenemos nuestro dataset preparado, es hora de definir la matriz X y el vector y, además de separar en set de train y de test con los que probaremos los clasificadores de noticias."
      ],
      "metadata": {
        "id": "QvB0kXEL4exF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "6rpSplIm4b30"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nos interesaba para esta entrega final analizar los efectos de considerar además del contenido de la noticia a sus titulares. Para esto, se definirá como `X` a la matriz de datos que considera solamente los textos de las noticias para clasificarlas..."
      ],
      "metadata": {
        "id": "RiMiub0CCBno"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = text\n",
        "y = df_all[['is_fake', 'subject']]"
      ],
      "metadata": {
        "id": "6lpC-Hln4_cU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "... y se definirá como `X2` a la matriz de datos que considera tanto el título de la noticia como su contenido de texto para clasificar las noticias."
      ],
      "metadata": {
        "id": "dSKc_iAHGg3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X2 = textPlusTitle"
      ],
      "metadata": {
        "id": "AOrz_jXGbACQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación se presentan las dimensiones de la matriz `X` y de la matriz `X2`."
      ],
      "metadata": {
        "id": "Pcj4VJX9HUFE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LxA-d8jISiN",
        "outputId": "1c08cd1e-f8db-472b-d120-3c54c85c2428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39099, 300)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PrvZMREH1Jb",
        "outputId": "6e034333-bbf6-4c0f-cc05-77cc8e176e6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39099, 300)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dado que 300 dimensiones es un número de features razonable, no será necesario aplicar técnicas de reducción de datos. \n",
        "\n",
        "Recordemos, además, que después de la etapa de procesamiento de datos nos quedamos un dataset de 39.099 noticias, de diferentes temáticas."
      ],
      "metadata": {
        "id": "sT5wHT61Hn_G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Procedemos a separar el set de train y el set de test en una proporción 80/20, para ambas matrices `X` y `X2`:"
      ],
      "metadata": {
        "id": "yfWhBZKJ5Gf0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2,random_state=34)\n",
        "print('\\nTotal number of samples in the Train Dataset :', Xtrain.shape[0])\n",
        "print('Total number of samples in the Test Dataset :', Xtest.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSwgV0NVIQ4U",
        "outputId": "f1e6b346-10b7-44ef-e88d-f6ea74ceea2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Total number of samples in the Train Dataset : 31279\n",
            "Total number of samples in the Test Dataset : 7820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X2train, X2test, y2train, y2test = train_test_split(X2, y, test_size=0.2,random_state=34)\n",
        "print('\\nTotal number of samples in the Train Dataset :', X2train.shape[0])\n",
        "print('Total number of samples in the Test Dataset :', X2test.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6glU_6MSbNA2",
        "outputId": "cf081e9b-6aa8-471d-9b23-0968ad9d4abe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Total number of samples in the Train Dataset : 31279\n",
            "Total number of samples in the Test Dataset : 7820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4) Entrenamiento y resultados"
      ],
      "metadata": {
        "id": "wlK8qRvrIWsG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Naive Bayes**\n",
        "\n",
        "El clasificador Bayesiano ingenuo es un clasificador probabilístico basado en la Teoría de Bayes. Su algoritmo, dada una instancia de una clase (es decir, dados los valores de sus atributos), busca maximizar la probabilidad de ocurrencia de la clase real de esa instancia. \n",
        "\n",
        "Este es un algoritmos de aprendizaje de máquina\n",
        "muy utilizado en la práctica. Tiene aplicaciones\n",
        "exitosas en diagnósticos médicos, clasificación de\n",
        "texto, entre otros.\n",
        "\n",
        "En esta ocasión, el algoritmo calculará la probabilidad de que cada noticia pueda ser considerada falsa o verdadera, y finalmente tomará la decisión en base a cuál máximiza esta probabilidad. Creemos que Naive Bayes será sumamente útil para determinar si un texto es considerado como falso o no. Esto se debe a que la matriz X generada tiene columnas independientes gracias al vectorizer, pues cada columna será un conteo de cuántas veces aparece una cierta palabra en el texto, y cada palabra sería independiente de otra gracias al procesamiento de lenguaje natural que ya se hizo. Además, Naive Bayes es muy rápido aún si el dataset es extenso, lo cual nos favorece pues tenemos un gran dataset de noticias."
      ],
      "metadata": {
        "id": "pJGtmLryI8SC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación se aplica Naive Bayes considerando **sólo los textos** de las noticias:"
      ],
      "metadata": {
        "id": "Qutt7tvlJEG4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bnb = GaussianNB().fit(Xtrain, ytrain['is_fake'])\n",
        "print('\\nAccuracy score :',bnb.score(Xtest, ytest['is_fake']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiPA7XXLIQUg",
        "outputId": "da0c597d-a546-48c1-91fc-36dc75634092"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score : 0.8985933503836318\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "y ahora se aplica Naive Bayes considerando tanto los **títulos** como los **textos** de las noticias:"
      ],
      "metadata": {
        "id": "Jx5RSAWJJVHC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bnb2 = GaussianNB().fit(X2train, y2train['is_fake'])\n",
        "print('\\nAccuracy score :',bnb2.score(X2test, y2test['is_fake']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnXiRU5DJgWb",
        "outputId": "b10e343c-f1a4-47c2-f05d-1d3d2d296175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score : 0.8843989769820972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos observar que, pese a que los accuracys obtenidos son bastante similares, al agregar los títulos de las noticias el accuracy baja de un **89.85%** a un **88.43%**. Veamos un poco más de cerca los resultados:"
      ],
      "metadata": {
        "id": "1EHwuckPJ7j_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Matrices de confusión:**"
      ],
      "metadata": {
        "id": "aJwJzRtbSIQJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = bnb.predict(Xtest)\n",
        "y2pred = bnb2.predict(X2test)\n",
        "cm = confusion_matrix(ytest['is_fake'], ypred)\n",
        "cm2 = confusion_matrix(y2test['is_fake'], y2pred)"
      ],
      "metadata": {
        "id": "70rTa56Hh4__"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(2, figsize=(5,7))\n",
        "categorias = ['Real', 'Fake'] #0: Real, 1: Fake\n",
        "\n",
        "sns.heatmap(cm, annot=True, ax = ax1, fmt='g');\n",
        "ax1.set_xlabel('Predicted labels');\n",
        "ax1.set_ylabel('True labels'); \n",
        "ax1.set_title('Confusion Matrix Naive Bayes \\n (only text)'); \n",
        "ax1.xaxis.set_ticklabels(categorias); \n",
        "ax1.yaxis.set_ticklabels(categorias);\n",
        "\n",
        "sns.heatmap(cm2, annot=True, ax = ax2, fmt='g');\n",
        "ax2.set_xlabel('Predicted labels');\n",
        "ax2.set_ylabel('True labels'); \n",
        "ax2.set_title('Confusion Matrix Naive Bayes \\n (title + text)'); \n",
        "ax2.xaxis.set_ticklabels(categorias); \n",
        "ax2.yaxis.set_ticklabels(categorias);\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "fig.savefig(\"CM-NB.pdf\", bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "5H26eKkYh62V",
        "outputId": "e6c8c3d1-afa0-43cf-b4cd-94e0bbfdf5ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yhNP6XcSECZ9X86uoYfKQYrE6FTrYtjSzeySdZ2b/A/4naUzWvdYi9erV5d5Bf6FRo4YsKyvj5DMvZs9du3D9zXcw6C9X8esOG/PwE8/w7/uGMfCKi7j59rs5tOe+9D5wf94dN56//+s+/nLVJTRoUJ8br7yYTdq3ZcbMWRx96jnsvsuONG3SuLpfokthyZIl7NfjaBYtWkydOnV4/bXhPP/8q2yxxaa0a7cRW23dDTOjdeuWAAy49Bw+/HACRx51Gptv/mv+cduN9Oh5TDW/ihomD5d+SWoAvA7UJ8S//5rZ1ZI6Ag8DLYFxwElmtjRO/Hg/sCMwCzjGzL6JdQ0ATgXKgXPNLONMDYWeFmdZ/H+6pIMkbQ+0KPAxaxRJNGrUEICysjLKysqQhIBFixYDsGDhIlq3Cr90X339HTvvuB0AO++wLa++MRqADhu3Y5P2YfLO9Vu3pMV6zZkzd16RX42risTPt27dOtSpWxcz44zTT+aGgbdiMXDMnDkLgN/8ZjNeffUtAD7//Cs22aQd66/fqnoaXlOVlWdfslsC7GNm2wLbAT3jdDc3Abea2abAHEIQJf4/J5bfGrdD0pbAscBWhCnM/xlnf0ir0MH2BknNgIuAi4G7gQsKfMwap7y8nN/16U+3g4+j607bs81WW3DtZedz5sVXse9hJ/L0C6M47aSjANi8cyde/l/4pXv5f2+zaPHPzJ03f6X6Pp74OcuWldG+7YZFfy0ud7Vq1WLsmBeZPvUjRo16nffGfECnTh04+qhDeWf0SJ4ZMZRNN+0IwEcfT+Twww4EYKcu27HJJu1o5z/flVlF9iVbFcHC+LRuXAzYB/hvLB9CmIcMoHd8Tly/b5ynrDfwsJktMbOvCdPmJCaJTKmgwdbMnjGzeWb2iZntbWY7mtmIdNsnz/l+9/3DCtm0oqpduzaPD7mDUcOH8vHEL/hy8jfc/8hw7rz5OkY9+QCHHdiD/xt0FwAX9z+NsR98zJGn9Gfs+I9p07oltWqt+DHN/Gk2A677Kzf86YKVyl3NU1FRQZederBJxy7s1GV7ttpqc+rXr8cvvyxh164Hcve9D3H34DCOe9P/3U6z5k0ZO+ZF+vf/PR+M/4TyEr+IP9+srDzrkhxD4tKvcj2SaksaD8wAXgK+AuaaWWJQeArQNj5uC3wPENfPIww1LC9PsU9Khb4aYTPgTqCNmW0taRvgUDO7IdX2yXO+L/tpcmmfekyhaZPG7LzDNrwxeiyfT5rMNlttAUCvfbtx+kVXAGGI4LY/XwnA4sU/8/Jrby4fl124aBFnXXIV557eh223/k31vAhXZfPmzee1/73FAT26M2XqdIY/ORKAJ598jnvuugWABQsWctofLly+z6Qv3mHy5G+rpb01Vg5XIyTHkAzblAPbxVl2hwNb5KV9WRS6a3QXMIA4dhsnWzu2wMesUWbPmcv8BeFbyy9LljB6zAd06tCehYsW8813UwB4e8wHdNpkYwDmzJ1HRezR3DX0EQ4/qAcAy5Yt47wB13Noz33psfee1fBKXFW0atWCZs2aAtCgQQP227cbn3/+FSNGPE/3vXYDYK9uXfniy8kANGvWlLp16wJw6u+P540332XBgoWpK19XlZdnX6rAzOYCrwJdgeaSEp3PdsDU+Hgq0B4grm9GOFG2vDzFPikV+mqERmb2XhjiWK60r9+oopmz5nD5DTdTXlGBVRgH7LMn3XffhWsuPZcLLh+IaommTRpz/YAwlD3mg4/4+7/uQxI7brs1V1x0FgDPv/IG48Z/wtx5C3hy5MsADLz8QrbY7NfV9tpcehtu2IZ77/k7tWvXolatWvz3v0/z7MiXefOt9xg65HbOO+8PLFq4mNPPuASA32zRmXvv/TtmxsSJn/OHfhdX8yuogfIwrBKv/V9mZnMlNQT2J5z0ehU4knBFQh/gqbjLiPh8dFz/ipmZpBHAQ5JuATYCOgPvZTy2FTCTjqTngLOBx8xsB0lHAqeaWa9s+66NwwgutYYbeU99XVG2dKqyb5XaoiuPzhoTfnX9oxnrj0OZQ4DahG/2j5rZdZI6EQJtC+AD4EQzWxIvFRtKmP58NnCsmU2OdV0O/J7QgTzfzJ7LdOxC92z7E8ZPtpA0FfgaOKHAx3TOrY3ycAdZHMrcPkX5ZFJcTWBmvwBHpalrIDAw12MXNNjGF7CfpF8R/oosJozZ+si/c65KLLfraGusgpwgk9RU0gBJt0vanxBk+xCuRTu6EMd0zq3lKiz7UoMVqmc7lHAXxmjgD8DlgIDDzWx8gY7pnFub+YSPKXUys98CSLobmA5sHMc/nHOuyjyfbWqJnAiYWbmkKR5onXNrxINtSttKStzQL6BhfC7C7clNC3Rc59zaqsRPkBVqpoaM2W+cc67KvGfrnHOFZ+WlnZjHg61zrjR4z9Y55wrPyrxn65xzhVfasdaDrXOuNJR6z7ZKt+tKWi9mzXHOueKqyGGpwbL2bCW9Bhwatx0HzJD0lpldmHFH55zLIysr7RNkufRsm5nZfOAI4H4z2wXYr7DNcs65lVmFZV1qslyCbR1JGxKydT1T4PY451xKVpZ9qclyCbbXAS8Ak8xsTMxo/mVhm+Wcc5XkYcxWUntJr0qaKGmCpPNi+TWSpkoaH5cDk/YZIGmSpM8lHZBU3jOWTZJ0WbZjZx2zNbPHgMeSnk8Gfpf9ZTnnXP7kqedaBlxkZu9LagKMk/RSXHermd2cvLGkLQkTHmxFmGvs5ThrOMAdhDnMpgBjJI0ws4npDpw22Er6B5B2EMTMzs3+upxzLj8sD1cbmNl0QspXzGyBpE+Bthl26Q08bGZLgK8lTWLF9DmTkuYjezhuW/VgC4zN/SU451xhVeTQs5XUD+iXVDTYzAan2bYDYT6yd4HdgbMlnUyIfReZ2RxCIH4nabcprAjO31cq3yVT29IGWzMbUqlhjcxscabKnHOuYCz7xLwxsKYMrskkNQYeJ8yKO1/SncD1hG/z1wN/I8ycmzdZT5BJ6ippIvBZfL6tpH/msxHOOZeNVWRfciGpLiHQPmhmTwCY2Y9mVm5mFcBdrBgqmAq0T9q9XSxLV55WLlcj/B04AJgVG/Uh0C2H/ZxzLm8qypR1yUaSgHuAT83slqTyDZM2Oxz4JD4eARwrqb6kjkBn4D1gDNBZUkdJ9Qgn0UZkOnZOuRHM7PvQxuVKO2W6c67kWA7DCDnYHTgJ+FhSYvLZPwHHSdqOMIzwDXB6OKZNkPQo4cRXGdDfzMoBJJ1NuCy2NnCvmU3IdOBcgu33knYDLHa/zwM+rdrrc865NZNLzzUbM3uTMD1XZSMz7DMQGJiifGSm/SrLZRjhDKA/4QzcNGC7+Nw554rGLPtSk+VyU8NPwAlFaItzzqVVUValJIU1Ti5XI3SS9LSkmZJmSHoq3rLrnHNFU+o921z+VDwEPApsSLhd7TFgWCEb5ZxzlVWU18q61GS5tK6RmQ01s7K4PAA0KHTDnHMuWb6us60umXIjtIgPn4sZbR4mXBZxDFU4A+ecc/lQXlGze67ZZDpBNo4QXBOXSZyetM6AAYVqlHPOVWYVebnOttpkyo3QsZgNcc65TCrK19Jgm0zS1sCWJI3Vmtn9hWqUc85VVpGfO8iqTS4TPl4NdCcE25FAL+BNwIOtc65oKkp8GCGXEecjgX2BH8ysL7At0KygrXLOuUoqTFmXmiyXYYSfzaxCUpmkpsAMVk4t5pxzBVfqPdtcgu1YSc0JOR7HAQuB0QVtFdC43V6FPoSrIebfdkR1N8GVgJrec80ml9wIZ8WH/5L0PNDUzD4qbLOcc25la+11tpJ2yLTOzN4vTJOcc25VNTz1QVaZerZ/y7DOgH3y3BbnnEtrrR1GMLO9i9kQ55zLpDwPwVZSe8Jlq20IncbBZnZbTE/wCNCBMFPD0WY2J06jcxtwILAYOCXxrV5SH+CKWPUNlSfJray0B0Gcc+sMQ1mXHJQRpinfEtgV6C9pS+AyYJSZdQZGxecQ7ivoHJd+wJ2wPHfM1YTpy3cGrpa0XqYDe7B1zpWEMlPWJRszm57omZrZAsIUX22B3kCiZzoEOCw+7g3cb8E7QPM4OeQBwEtmNtvM5gAvAT0zHduDrXOuJOTSs5XUT9LYpKVfuvokdQC2B94F2pjZ9LjqB8IwA4RA/H3SblNiWbrytHK5XVeEaXE6mdl1kjYGNjCz97Lt65xz+VKewzCBmQ0GBmfbTlJj4HHgfDObnzx7uJmZpLxf/JBLz/afQFfguPh8AXBHvhvinHOZVOSw5CLOEv448KCZPRGLf4zDA8T/Z8Tyqax8x2y7WJauPK1cgu0uZtYf+AUgjk/Uy2E/55zLm3KUdckmflO/B/jUzG5JWjUC6BMf9wGeSio/WcGuwLw43PAC0EPSevHEWI9YllYut+suk1SbeE2xpNbk/kfEOefyIk+pEXYHTgI+ljQ+lv0J+AvwqKRTgW+Bo+O6kYTLviYRLv3qC2BmsyVdD4yJ211nZrMzHTiXYDsIGA6sL2kgIQvYFZl3cc65/Mql55qNmb0JaSvaN8X2BvRPU9e9wL25HjuX3AgPShoXGyLgMDP7NNcDOOdcPpT61+lcrkbYmNB9fjq5zMy+K2TDnHMuWbnW0tt1kzzLiokfGwAdgc+BrQrYLuecW8la37M1s98mP4/ZwM5Ks7lzzhVE2TrQs12Jmb0vaZdCNMY559JZm1MsAiDpwqSntYAdgGkFa5FzzqVQVtod25x6tk2SHpcRxnAfL0xznHMutbW6ZxtvZmhiZhcXqT3OOZfSWtuzlVTHzMok7V7MBjnnXCpr89UI7xHGZ8dLGgE8BixKrExK4OCccwVX4rPi5DRm2wCYRZhzLHG9rQEebJ1zRVNW3Q1YQ5mC7frxSoRPWBFkE0p9rNo5V2JKPehkCra1gcakTtpQ6q/bOVdi1toTZMB0M7uuaC1xzrkM1uYTZCX+d8Q5tzYpL/GIlGmmhlVyOzrnXHXJx7Q4ku6VNEPSJ0ll10iaKml8XA5MWjdA0iRJn0s6IKm8ZyybJOmyysdJJW2wzZZ13Dnniqkcy7rk4D5STzl+q5ltF5eRAJK2BI4lZDjsCfxTUu14s9cdQC9gS+C4uG1GVU5E45xz1SEfY7Zm9nqcwjwXvYGHzWwJ8LWkScDOcd0kM5sMIOnhuO3ETJXlMuGjc85Vu/IcFkn9JI1NWvrlWP3Zkj6KwwzrxbK2wPdJ20yJZenKM/Jg65wrCRXKvpjZYDPrkrQMzqHqO4FfA9sB04G/FaL9PozgnCsJOY7JVpmZ/Zh4LOku4Jn4dCrQPmnTdrGMDOVpec/WOVcS8nE1QiqSNkx6ejjhrlmAEcCxkupL6gh0JuSMGQN0ltRRUj3CSbQR2Y7jPVvnXEnIR89W0jCgO9BK0hTgaqC7pO0Id8Z+A5wOYGYTJD1KOPFVBvQ3s/JYz9nAC4Q7be81swnZjl3QYCtpM8J4SBsz21rSNsChZnZDIY/rnFv75OlqhONSFN+TYfuBwMAU5SOBkVU5dqGHEe4CBgDLAMzsI0KX2znnqiRP19lWm0IPIzQys/e08qyYpZ4pzTlXDdbm3Aj58JOkXxOzhEk6knBphXPOVUlN77lmU+hg2x8YDGwhaSrwNXBCgY9Z49WqVYvRbz/LtGk/cPgRfbnvvkHsuMM2LFtWxpix4+nf/zLKylZ8Adhxx215/X9PcuJJ/Rk+vErDRK6IlpSVc+pjY1haXkF5hbFf5zac2XVTHh7/HQ998C3fz/uZV07vznoN6wHw6lczuHP0JISoXUtcstfmbN82XE+/420vsmnLMNfqBk0bcNuh21fb66opKjzYZrSeme0n6VdALTNbIOlg4NsCH7dGO+fsU/ns80k0bdIYgIeHDeeUU84F4P77b+f3fY9j8F1DgRCYBw4cwMsvv15t7XW5qVe7FoN/14VG9eqwrLyC3z/6Hrt3aMV2GzWnW8fWnPbfMSttv0v7FnTv1BVJfDFzAZeO/JDhffYAoH6d2jxyYtfqeBk1VmmH2iKcIJO0tZktioH2WODKAh+zRmvbdgN69dqH//xn2PKy5194dfnjsWPG07bdisv++p/VlyeHP8eMmbOK2k5XdZJoVC/0X8oqjLIKQ8AW6wKt2b4AACAASURBVDdlo2YNV9m+Ub06JM5n/LysHHlW04zKsKxLTVbonu2RwH8lHQ/sCZwM9CjwMWu0m/96DQP+dCNNmvxqlXV16tTh+OOP4KKLrwFgo4024NDePenR42gGdynIHYQuz8orjOMfeofv5y3mmG3a89sNm2fc/pVJP/KPt75k9uKlDOq9w/LypWUVHP/QO9SpJfp26cjem65f6KbXeFbDg2k2BQ22ZjY59mafBL4DepjZz+m2j0kj+gHUrtOc2rUbF7J5RXdgr32ZOXMWH3zwMd267brK+kGDBvLmm+/y1lvvAXDzX6/m8stvxKy0P2Trktq1xCMndmXBL8u48JnxTPppAZu2apJ2+302bcM+m7Zh3JTZ/HP0JP79uy4AjDx1T9Zv3IAp8xbT779j2bRVY9o3b1Ssl1Ej+QmyFCR9zMpDLC0Id1q8Kwkz2ybVfjFpxGCA+g3al/Y7m0LX3bpw0EH7c0DPvWlQvz5NmzbhP/+5jb59z+Pyy8+ndauWHN1/RR7iHXfchqFD7wCgVcsW9Dxgb8rLyhnx9AvV9RJcjpo0qEuXdi14+9tZGYNtwo7tWjD1xQnM+Xkp6zWsx/qNGwDQrlkjurRrwWcz56/zwdYv/Urt4ALVW9KuvPImrrzyJgC6dduVC84/nb59z6Nv32PZf7+96NnruJV6sZtvsfvyx3fddQsjR77sgbYGm714KXVriSYN6vJLWTnvfjeLU7p0TLv9d3MX075ZQyTx6Yz5LC2voHmDusz/ZRkN6tSmXp1azPl5KeOnz6VPlw7FeyE1VHmJf8MrSLA1s5WuNpC0PtCgEMdaG9z+jz/z3XdTef1/TwLw5FPPceONt1Vzq1xV/bRoCVe9+AkVZlSYsX/nDejWqTUPffAtQ8Z9w6xFSzn6gdHs0aEVV++/FaO+/JFnPp1GnVq1qF+nFjcduA2SmDx7EQNHTUQCM+jbpQO/brl2DamtjlK/9EuFHA+UdCghN+RGwAxgE+BTM9sq275r4zCCS23OrYdVdxNckTQ68x+rfcnFMZscljUmPPLtkzX2ko5CX/p1PbAr8IWZdSRMIvlOgY/pnFsLVWBZl5qs0MF2mZnNAmpJqmVmrwJdCnxM59xayBPRZDZXUmPgdeBBSTOARQU+pnNuLVTql0AWpGcraeP4sDewGLgAeB74CjikEMd0zq3dSv0OskINIzwJYGaLgMfMrMzMhpjZoDis4JxzVWI5/Msmzp47Q9InSWUtJL0k6cv4/3qxXJIGSZoUZ97dIWmfPnH7LyX1yaX9hQq2yWcEOxXoGM65dUi5VWRdcnAf0LNS2WXAKDPrDIyKzwF6EeYd60y4s/VOCMGZMJ3OLsDOwNVJ05+nVahga2keO+fcasnHhI9m9jowu1Jxb2BIfDwEOCyp/H4L3gGax8khDwBeMrPZZjYHeIlVA/gqCnWCbFtJ8wk93IbxMfG5mVnTAh3XObeWKs8hnCbnV4kGxzQAmbQxs8SkBj8AbeLjtsD3SdtNiWXpyjMq1B1ktQtRr3Nu3ZXL1QjJ+VVW8xgmqSDfxgt9na1zzuVFAW9q+DEODxD/nxHLpwLtk7ZrF8vSlWfkwdY5VxLydIIslRFA4oqCPsBTSeUnx6sSdgXmxeGGF4AektaLJ8Z6xLKMCn1Tg3PO5UU+vttLGgZ0B1pJmkK4quAvwKOSTiVM2XV03HwkcCAwiXC/QF8AM5st6XogMc/RdWZW+aTbKjzYOudKQlkeMtqa2XFpVu2bYlsjTFqbqp57gXurcmwPts65klDqt+t6sHXOlYRcLv2qyTzYOudKgvdsnXOuCNbgaoMawYOtc64k1PTk4Nl4sHXOlQTv2TrnXBHkkkKxJvNg65wrCd6zdc65IqjwqxGcc67wvGfrnHNF4GO2zjlXBOVWXt1NWCMebJ1zJcHvIHPOuSLwMVvnnCsCvxrBOeeKoNRPkPm0OM65kpCvaXEkfSPpY0njJY2NZS0kvSTpy/j/erFckgZJmiTpI0k7rG77Pdg650qCmWVdqmBvM9vOzLrE55cBo8ysMzAqPgfoBXSOSz/gztVtvwdb51xJKK+oyLqsgd7AkPh4CHBYUvn9FrwDNE/MxFtVHmydcyUhl6nMJfWTNDZp6ZeiKgNelDQuaX2bOHMuwA9Am/i4LfB90r5TYlmV+Qky51xJyKXnamaDgcFZNtvDzKZKWh94SdJnleowSXk/G+c9W+dcSbAc/uVUj9nU+P8MYDiwM/BjYngg/j8jbj4VaJ+0e7tYVmUebJ1zJSEfY7aSfiWpSeIx0AP4BBgB9Imb9QGeio9HACfHqxJ2BeYlDTdUiQ8jOOdKQp6us20DDJcEIf49ZGbPSxoDPCrpVOBb4Oi4/UjgQGASsBjou7oH9mDrnCsJFWt2tQEAZjYZ2DZF+Sxg3xTlBvRf4wMDKvXkDmsbSf3iIL9by/nPet3iY7Y1T6pLVdzayX/W6xAPts45VwQebJ1zrgg82NY8Poa37vCf9TrET5A551wReM/WOeeKwIOtc84VgQfbIpFUHpMVfyLpaUnNV7OeUyTdnu/2ufxI+jknlg5ptusg6ZPits5VJw+2xfNzTFa8NTCbPN2V4mqcxM85sXxT3Q1yNYMH2+oxmpgTU9KvJT0fc2u+IWmLWH6IpHclfSDpZUltMtboaiRJjSWNkvR+nIqld4ptOsWf807pPg+u9HluhCKTVJtwD/Y9sWgwcIaZfSlpF+CfwD7Am8CuMbfmacAfgYuqo82uShpKGh8ffw0cBRxuZvMltQLekTQisbGkzYGHgVPM7ENJo0j9eXAlzoNt8SR+CdsCnxKSFjcGdgMei1mIAOrH/9sBj8TcmvUIv7iu5vvZzLZLPJFUF7hRUjeggvDzT3xLaU1I5XeEmU3M8nlwJc6DbfH8bGbbSWoEvEAYs70PmJv8y5nkH8AtZjZCUnfgmmI11OXVCYSguqOZLZP0DdAgrpsHfAfsAUwkDOul+zy4EudjtkVmZouBcwlDAouBryUdBcunTU6kf2vGiozwfVapyJWKZsCMGGj3BjZJWrcUOJyQnPp4M5tP+s+DK3EebKuBmX0AfAQcR+j5nCrpQ2ACYTZPCD3ZxySNA36qjna6vHgQ6CLpY+BkoPJ8V4uAg4ELJB1K+s+DK3F+u65zzhWB92ydc64IPNg651wReLB1zrki8GDrnHNF4MHWOeeKwIPtOqJS1rHH4s0Vq1vXfZKOjI/vlrRlhm27S9ptNY7xTby9NafyStssrOKxrpF0cVXb6FxVeLBddyRnHVsKnJG8UtJq3U1oZqeZ2cQMm3Qn3ILq3DrNg+266Q1g09jrfCMmRpkoqbakv0oaI+kjSafD8juZbpf0uaSXgfUTFUl6TVKX+LhnzG71Ycx01YEQ1C+Iveo9JbWW9Hg8xhhJu8d9W0p6UdIESXcDIgtJT8bsWBMk9au07tZYPkpS61iWNaOWpHMlTYyv/+HVe3udS8HMfFkHFmBh/L8OIfnJmYRe5yKgY1zXD7giPq4PjAU6AkcALwG1gY2AucCRcbvXgC6E+/+/T6qrRfz/GuDipHY8BOwRH28MfBofDwKuio8PAgxoleJ1fJMoTzpGQ+AToGV8bsAJ8fFVwO3x8Sigc3y8C/BK5TYC04D68XHz6v65+bL2LJ6IZt2RnPrvDUKKx92A98wskVGsB7BNYjyWcF9/Z6AbMMzMyoFpkl5JUf+uwOuJusxsdpp27AdsmZTVqmnMdtWNENQxs2clzcnhNZ0r6fD4uH1s6yxCdq1HYvkDwBNVyKj1EfCgpCeBJ3Nog3M58WC77lgp9R9ADDqLkouAc8zshUrbHZjHdtQi5On9JUVbchYzoe0HdDWzxZJeY0U2rcqM3DNqHUQI/IcAl0v6rZmVValxzqXgY7Yu2QvAmTEHK5I2k/Qr4HXgmDimuyGwd4p93wG6SeoY920RyxcATZK2exE4J/FEUiL4vQ4cH8t6AetlaWszYE4MtFsQetYJtYBE7/x44E3LIaOWpFpAezN7Fbg0HqNxlnY4lxMPti7Z3YS8qu8rTEb4b8K3n+HAl3Hd/YRpfVZiZjMJY75PxIxVia/xTwOHJ06QEdJLdoknoCay4qqIawnBegJhOOG7LG19Hqgj6VPgL4Rgn7AI2Dm+hn2A62J5toxatYEHYoauD4BBZjY3Szucy4ln/XLOuSLwnq1zzhWBB1vnnCsCD7bOOVcEHmydc64IPNg651wReLB1zrki8GDrnHNF4MHWOeeKwIOtc84VgQdb55wrAg+2zjlXBB5snXOuCDzYVgNJDSU9LWmepMfWoJ4TJL2Yz7ZVB0nPSepTTcdeKKlTdRzbrVs82GYg6XhJY+Mv5PQYFPbIQ9VHAm0I07gctbqVmNmDZtYjD+1ZSZybzCQNr1S+bSx/Lcd6rpH0QLbtzKyXmQ1ZjXaeEtvzx0rlU2Jy8azMrLGZTa7qsbO0q7ukivi5WShpqqRr83kMV3o82KYh6ULg78CNhMC4MfBPVs2Bujo2Ab6o4TMAzAS6SmqZVNYH+CJfB4gJvNf0Mzgb+KOkJlm3LK5pMZA3BvYg5NE9rLob5aqPB9sUJDUjJJzub2ZPmNkiM1tmZk+b2SVxm/qS/i5pWlz+Lql+XNc99q4ukjQj9or7xnXXEiYhPCb2ek6t3AOU1CH22OrE56dImixpgaSvJZ2QVP5m0n67KcxYOy/+v1vSutckXS/prVjPi5JaZXgblhLm4Do27l8bOAZ4sNJ7dZuk7yXNV5i1ds9Y3hP4U9Lr/DCpHQMlvQUsBjrFstPi+jslPZ5U/00KM+SmmzfnU0Iy8wtTrZS0s6TRkubGn8PtkuolrTdJm0raRdIP8XUm1h0u6aP4uJakyyR9JWmWpEe1YjaKjOK8bG8DW+bwvm0gaXHyHzlJO0iaqRUzaPxe0qeS5kh6QdImsVwKswrPiPV+LGnrXNroCs+DbWpdCfNZDc+wzeWEqVi2A7YFdgauSFq/AWFalbbAqcAdktYzs6sJveVHYs/nnkwNUZiWZhDQy8yaECYtHJ9iuxbAs3HblsAtwLOVeqbHA30JU5HXAy7OdGzCrAwnx8cHEGawnVZpmzGE96AFYebcxyQ1MLPnK73O5CloTiLM6tAE+LZSfRcBv41/SPYkvHd9LHOW+yuB89MEv3LgAqAV4ee6L3BW5Y3M7F3CDA/7JBUfH18ThKl8DgP2IswwPAe4I0OblpPUGdidlWeTSPe+/UCYsfjopG1PAh42s2WSehP+iB1BmNH4DWBY3K4HYf60zQifvaMJE2C6GsCDbWotgZ+yfM0/AbjOzGbEKWGuJfxSJCyL65eZ2UhgIbD5aranAthaUkMzm25mE1JscxDwpZkNNbMyMxsGfEaYuDDhP2b2hZn9DDxK+GVPy8zeBlpI2pwQdO9Psc0DZjYrHvNvhBlrs73O+8xsQtxnWaX6FhPex1sIM+OeY2ZTsrRzPGGq9UtTrBtnZu/EY31DmOpnrzRVDQOOA4jDEgeyIpCdAVxuZlPMbAlh+vMjE98+Utgo9qbnE4Ze3gWWfwvJ8r4NAU6M7agd2zQ0qR1/NrNP4+fzRmC72LtdRvgDtgVhFpZPzWx6mva5IvNgm9osoFWGXyQIvZvkXtm3sWx5HZWC9WJWY/JAM1tE+Pp+BjBd0rMKExxma0+iTW2Tnv+wGu0ZCpxNmORxlZ6+pIvjV9p5kuYSelSZhicAvs+0MvYyJxNm+300hzZCGJo5U1KbSu3bTNIzcYhgPiE4pWvfQ8ARcTjoCOB9M0u8p5sAw2MAnUsYvignjOenMs3MmptZU6A58DMhiCbalel9e4ow3XtHYH9gnpm9l9SO25LaMZvwPrU1s1eA2wk97hmSBktqmu2Nc8XhwTa10cASwtfGdKYRPvgJG7PqV+xcLQIaJT3fIHmlmb1gZvsDGxJ6q3fl0J5Em6auZpsShhK+do+Mvc7l4tf8PxK+rq5nZs2BeYRffghTiKeSceI7Sf0JPb1psf6szOwz4AnC8E6yOwnvWecY+P6U1L7KdUwk/IHqxcpDCBD+QPSKATSxNDCzrO+vmc2LdR0SX1/G9y1O8/4ooXd7Eit6tYl2nF6pHQ3jtxDMbJCZ7UgYH94MuCRb+1xxeLBNIf5yXEUYZz1MUiNJdSX1kvR/cbNhwBWSWscTTVcRvvaujvGEmWU3Vjg5NyCxQlIbSb3j2O0SwnBERYo6RgKbKVyuVkfSMYRfuGdWs03A8pM7e7FqEIPwlbWMcOVCHUlXAck9qR+BDqrCFQeSNgNuYEWg+aNWTHeezbWEMenmldo4H1gYvxGcmaWOh4DzCGOfyddA/wsYmHQyqnUcP81KUmPCicbE8E+29w3CkM0pwKGsHGz/BQyQtFWsu5lWTM++UzzRV5fwB/wXUn9WXDXwYJtGHEe7kHDSayahR3E24Qw9hIAwFvgI+Bh4P5atzrFeIkz9/REwjpUDZK3YjmmEr4x7kSJgmNks4GDCCaZZhJ7TwWb20+q0qVLdb5pZql77C4Qpxb8g9Ah/YeUhgkSwmiXp/WzHicM2DwA3mdmHZvYloSc6NH61z9bOrwmB6VdJxRcTeqkLCN8IHkmxa7JhhPf4lUrv3W3ACOBFSQsIJ7t2yVDPRorX2RLemxaEcX7I/r5hZm8RAmXyUAZmNhy4CXg4Dot8QuiJQwjYdxFO3n1L+Bz8NcvrdUXiU5k7V0NJegV4yMzuru62uDXnwda5GkjSToQrLNqb2YLqbo9bcz6M4FwNI2kI8DJwvgfatYf3bJ1zrgi8Z+ucc0XgwXYdJ2mYMiRIkfQvSVdmWJ9TZq9SIWkbSW9Xdzvc2seD7TpM0jaEvA5PxecrJbYBMLMzzOz6uL67pIy3zhaDYvKYQtRlZh8BcyUdkmE356rMg+267XTgwSxJXooi9pCvqe52RA8S3hvn8saD7bqtF/A/AEm/Idyd1DVejD83lt8n6YZ4B9tzJF2sL2mjyhVK2lXS2/He/Q+VYxLvXEl6PT78MLbhmFh+sKTx8bhvx147ko5RSEvZND7vFfMktE5XFyHr1r653EjhXK482K6jYvDsCHwOYGafEpLdjI4pEZNveU0kxOlFUlLsyneVSWpLSPN4A+GOqYuBxyW1zle7zaxbfLhtbMMjkrYH7iX0RlsSMnuNkFTfzB4h5JIdpJBu8h7gNDObmaqueIyphAxaq5ulzblVeLBddyWCaT6v4zyRkLBmpJlVxNuQxxJSFRZSP+DfZvaumZVbmGJnCSHfMEB/Qp7a14CnzSyXfBELWDnHgnNrJFMKQbd2mxv/b0K4Nz8fNgGOqnRyqS7waqqNJT1DmDIGQrJ2JJ0fn79pZgdX4bh9JJ2TVFaPmPLSzOYqTKx5IfC7HOtswor3yLk15sF2HWVmiyR9RUjDNzNRnG23LOu/B4aa2R9ybMPyYJo4OWZm1+Syb4rjDjSzgalWxqxhvyckmRkE9MxUWRwOqUccYnEuH3wYYd02kpVnLfgRaKekOboq+RFoGdNApvIAcIikAyTVltQgXi7WLo9tTrQjefrxu4AzYnpBSfqVpIMkNZHUILbrT4T0i20lnZWhLliR9WtJntvt1mEebNdtg4ETpOWTKb5CyLn6g6RVUjPGBN3DgMnxrP9GldZ/T5h9+E+sSEt5Cfn/nF0DDIltONrMxgJ/IMxSMAeYRMgFC/Bn4HszuzMGzxOBGxTmBVulrlh2AuHKDOfyxnMjrOMkPQQ8amZPZt14HRAvGfu3mXWt7ra4tYsHW+ecKwIfRnDOuSLwYOucc0XgwdY554qgxl5nu+ynyT6YvI5ouNGe1d0EVyRlS6emnEY+F7nEhLqtOq12/YVWY4Otc86tpKK8uluwRjzYOudKg1VUdwvWiAdb51xJsPKy6m7CGvFg65wrDRXes3XOucIrX1bdLVgjHmydc6XBx2ydc67wfMzWOeeKwcdsnXOuCHzM1jnnisDHbJ1zrgh8zNY554qgxMdsPeuXc64kWMWyrEs2cV689yR9KGmCpGtj+X2SvpY0Pi7bxXJJGiRpkqSPJO2QVFcfSV/GpU+2Y3vP1jlXGvLTs10C7GNmCyXVBd6U9Fxcd4mZ/bfS9r2AznHZBbgT2EVSC+BqoAth1ulxkkaY2Zx0B/Zg65wrDXm4GsHCPGAL49O6ccmUurE3cH/c7x1JzSVtCHQHXjKz2QCSXgJ6EiZETcmHEZxzpcEqsi6S+kkam7T0q1yNpNqSxgMzCAHz3bhqYBwquFVS/VjWljBLdMKUWJauPC3v2TrnSkMOVyOY2WBgcJZtyoHtJDUHhkvaGhgA/ADUi/tfCly3pk1O5j1b51xpqKjIvlSBmc0FXgV6mtl0C5YA/wF2jptNBdon7dYulqUrT8uDrXOuNOQh2EpqHXu0SGoI7A98FsdhkSTgMOCTuMsI4OR4VcKuwDwzmw68APSQtJ6k9YAesSwtH0ZwzpUEy8/tuhsCQyTVJnQ2HzWzZyS9Iqk1IGA8cEbcfiRwIDAJWAz0BTCz2ZKuB8bE7a5LnCxLx4Otc6405OF2XTP7CNg+Rfk+abY3oH+adfcC9+Z6bA+2zrnS4LfrOudcEZT47boebJ1zpcF7ts45VwTes3XOuSLwnq1zzhWBJw93zrki8J6tc84VgY/ZOudcEZSXV3cL1ogHW+dcafCerXPOFYGP2TrnXBF4z9Y554rAx2ydc64IvGe7Kkn/IMMkamZ2biGO65xbe5n3bFMaW6B6nXPrKu/ZrsrMhhSiXufcOqzEb9ct6Bxkcb6fmyWNjNNOvCLplUIes6ZZsmQpx552Hkf0OYveJ5zO7XcPBeCdsR9wVN+z+V2f/px05kV8N2UaAGPHf8xRfc9m224H8eKrb6xU11MjX+LAY07lwGNO5amRLxX9tbiqq1WrFmPee4Gnhof+xz1338qXn49m7JgXGTvmRbbddisANt/817z5+ggWLZjMhRecXp1NrrnKyrMvWUhqIOk9SR9KmiDp2ljeUdK7kiZJekRSvVhePz6fFNd3SKprQCz/XNIB2Y5d6BNkDwKPAAcR5vTpA8ws8DFrlHr16nLvoL/QqFFDlpWVcfKZF7Pnrl24/uY7GPSXq/h1h415+Iln+Pd9wxh4xUVs2GZ9brj8Iu4b9vhK9cybv4A7//MQj9wzCIBjTj2X7nvsSrOmTarjZbkcnXvOaXz22Zc0bbLi53TpgBt44olnV9pu9uy5nH/BlfTu3bPYTSwd+RlGWALsY2YLJdUF3pT0HHAhcKuZPSzpX8CpwJ3x/zlmtqmkY4GbgGMkbQkcC2wFbAS8LGmzOE16SoWeXbelmd0DLDOz/5nZ74GUc/2srSTRqFFDAMrKyigrK0MSAhYtWgzAgoWLaN2qJQBtN2zD5pt2pJa0Uj1vvTuOrjttT7OmTWjWtAldd9qet94dV9TX4qqmbdsNObDXvtx777Cs286cOYux4z5k2bK8TGq4diovz75kEacrXxif1o2LEeLSf2P5EMIMuwC943Pi+n3jDLy9gYfNbImZfU2YEDIx/XlKhQ62iU/OdEkHSdoeaFHgY9Y45eXl/K5Pf7odfBxdd9qebbbagmsvO58zL76KfQ87kadfGMVpJx2VsY4fZ/7EBuu3Xv68TetW/Djzp0I33a2BW/52LZcNuIGKSj2y66+7lPfHvcTf/noN9erVq6bWlaAKy7pI6idpbNLSr3I1kmpLGg/MAF4CvgLmmlniFrUpQNv4uC3wPUBcPw9omVyeYp+UCh1sb5DUDLgIuBi4G7gg3cbJb9Td92fvDZSK2rVr8/iQOxg1fCgfT/yCLyd/w/2PDOfOm69j1JMPcNiBPfi/QXdVdzNdHh104H7MmPET73/w8Urll1/xZ7bauhu7dj2I9Vo054+XnFVNLSxBOfRszWywmXVJWgZXrsbMys1sO6AdoTe6RTGaX9Bga2bPmNk8M/vEzPY2sx3NbESG7Ze/UaedfFwhm1YtmjZpzM47bMMbo8fy+aTJbLNV+Bn32rcb4z+ZmHHfNq1b8cOMFcPdP878iTatWxW0vW717bZbFw45uAeTvniHBx/4J3vvvTtD7hvEDz/MAGDp0qUMGfIIO3VZZVZtl4ZVVGRdqlSf2VzgVaAr0FxS4hxWO2BqfDwVaA8Q1zcDZiWXp9gnpUJfjbCZpFGSPonPt5F0RSGPWdPMnjOX+QvCENEvS5YweswHdOrQnoWLFvPNd1MAeHvMB3TaZOOM9ey+y468/d77zJu/gHnzF/D2e++z+y47Frz9bvVcfsVf6NCpC5tutisnnHgWr776Fn1OOZcNNlh/+TaHHtqTCRM/q8ZWlpg8jNnGK6Sax8cNgf2BTwlB98i4WR/gqfh4RHxOXP+KmVksPzZerdAR6Ay8l+nYhb4a4S7gEuDfAGb2kaSHgBsKfNwaY+asOVx+w82UV1RgFcYB++xJ99134ZpLz+WCyweiWqJpk8ZcPyCMrnz86eecP+B65i9YyGtvvcsddz/AUw/+m2ZNm3D6Kcdx7GnnAXBG3+P9SoQSNHTI7bRq3QJJfPjhBM7qfxkAbdq05t3Rz9G0aWMqKio495w/8Nttu7NgwcIsNa5DKtLelFoVGwJDJNUmdDYfNbNnJE0EHpZ0A/ABcE/c/h5gqKRJwGzCFQiY2QRJjwITgTKgf6YrEQAUgnRhSBpjZjtJ+sDMto9l4+N4SUbLfppcuIa5GqXhRntWdxNckZQtnarsW/1/e/ceJlV15nv8+wNERFBAARUxgMEx3uNhhKhBjQ6iUVFjjMaMmGhIvMQZx2Sik0xUjBmfMyd64hidwcsjxAvq0SgmRkXUKJmgICoKmJF4BS+ooCCI0t3v+WOvwqLt7qrG2tVVze/js5/etfZlrbabl8Xaa7+rZat+dkLJmLD5xKkbfP+85d2zfUfSjqQ8CZKOA97IuU4z64zq/A2yvIPtmcAkYGdJS4CXgJNyrtPMOqEo4w2xfBU2KAAAGCtJREFUWpZrsI2IF4FDJG1ONj6ymmzM45U86zWzTqgyY7YdJpfZCJK2SO8NXynp78iC7HiytyyOz6NOM+vkKjAboSPl1bP9DbAc+DPwXeAngIBjIuLpnOo0s86sznu2eQXbYRGxO4Cka8keiu0QEWtyqs/MOrlo8AOylqzLphERjZIWO9Ca2Wfi5OEt2lPSirQvYLP0WWSJd7bIqV4z66zcs/20iOiax33NbOOV5wtY1eDVdc2sPvgBmZlZ/vyAzMysGtyzNTPLXzQ42JqZ5W9j6tlK6gsMjoh5ObXHzKxF9d6zLZkbQdIjKddBP2AucI2ky/JvmplZkaYythpWTiKaLSNiBXAsMCUiRgKH5NssM7P1RUOU3GpZOcG2m6RtybJ1/S7n9piZtSiaSm+lSBos6WFJCyTNl/QPqfxCSUskPZ22w4uuOV/SIkl/kXRoUfnYVLZI0nml6i5nzHYicD8wMyJmSxoGvFDGdWZmFRMNFblNA3BuRMyV1Bt4UtL0dOzyiPg/xSdL2oUsB/euwHbAg5J2Sod/TbZg5GJgtqRpEdHqMtklg21E3A7cXvT5ReBrZX9rZmaVUIEx2Yh4g7Q0V0SslLQQGNTGJeOAqRHxEfBSWvhxn3RsUYqHSJqazm1/sJX0H6S1w1pp9NltNNDMrKKayujZSpoATCgqmhQRk1o5dwjwReBxYD/gLEknA3PIer/LyQLxrKLLFvNJcH6tWfnIttrWVs92TlsXmplVUzljsimwthhci0nqBdwB/GNErJB0NXAxWQfzYuCXwHc+S3ubazXYRsTkZo3rGRGrK1m5mVm5orEyq5RL2oQs0N4UEXcCRMRbRcev4ZPJAEuAwUWXb5/KaKO8ReXMs/2SpAXA8+nznpKuKnWdmVklRZNKbqVIEnAdsDAiLisq37botGOA59L+NOAESZtKGgoMB54AZgPDJQ2V1J3sIdq0tuouZzbC/wUOLdwoIp6RNLqM68zMKqapMj3b/YC/B56VVFgP8V+AEyXtRTaM8DLwPYCImC/pNrIHXw3AmRHRCCDpLLKZWl2B6yNiflsVl/W6bkS8lv2FsE5tL2NpZp1OOWO2Je8RMZNsxZjm7m3jmkuAS1oov7et65orJ9i+JmlfINJYxz8AC8utwMysEirUs+0w5QTb7wO/Ipvu8DpZt/nMPBtlZtZcOWOytayclxreAU6qQlvMzFpV78G2nNkIwyTdI+ltSUsl3Z1e2TUzq5qmRpXcalk5iWhuBm4DtiV7N/h24JY8G2Vm1lyESm61rJxg2zMifhMRDWm7EeiRd8PMzIo1NqrkVsvayo3QL+3+IaUPm0o2B+0btGO6g5lZJdR6z7WUth6QPUkWXAvf4feKjgVwfl6NMjNrrtbHZEtpKzfC0Go2xMysLfU+G6GsN8gk7QbsQtFYbURMyatRZmbNNTaV84ipdpUMtpIuAA4kC7b3AocBMwEHWzOrmqjtJcZKKueviuOAg4E3I+LbwJ7Alrm2ysysmcamLiW3WlbOMMKHEdEkqUHSFsBS1s/jmIvNtvty3lVYjVhx5fEd3QSrA/Xesy0n2M6R1Ae4hmyGwgfAn3NtlZlZM7Xecy2lnNwIZ6Td/5R0H7BFRMzLt1lmZutr6qzzbCXt3daxiJibT5PMzD6tsbMGW7IFz1oTwFcq3BYzs1ZV4g0ySYPJZlINJItjkyLiV+mN2VuBIWQrNRwfEcvTMjq/Ag4HVgOnFDqaksYDP023/nnzdRuba+ulhoM+yzdlZlZJjS0usNBuDWTLlM+V1Bt4UtJ04BRgRkRcmtITnAf8mGyq6/C0jQSuBkam4HwBMIIsaD8paVpa/rxF9T3ibGYbjaYovZUSEW8UeqYRsZJs1ZlBwDig0DOdDByd9scBUyIzC+iTFoc8FJgeEctSgJ0OjG2r7rLeIDMz62hNlenZriNpCPBF4HFgYES8kQ69STbMAFkgfq3ossWprLXyVrlna2Z1oRGV3CRNkDSnaJvQ0r0k9QLuAP4xIlYUH4uIIBsaqKhyXtcV2bI4wyJioqQdgG0i4olKN8bMrDVRRs82IiYBk9o6Jy1cewdwU0TcmYrfkrRtRLyRhgmWpvIlrP8S1/apbAlZGoPi8kfaqrecnu1VwJeAE9PnlcCvy7jOzKxiGsrYSkmdx+uAhRFxWdGhacD4tD8euLuo/GRlRgHvp+GG+4ExkvpK6guMSWWtKmfMdmRE7C3pKYA0HaJ7GdeZmVVMOT3bMuwH/D3wrKSnU9m/AJcCt0k6FXgFKLxDfi/ZtK9FZFO/vg0QEcskXQzMTudNjIhlbVVcTrBdK6kraQxDUn+gqcxvzMysIhr02YNtRMyEVqP2wS2cH8CZrdzreuD6cusuZxjhCuC3wABJl5ClV/xFuRWYmVVClLHVsnJyI9wk6UmyqC/g6IhYmHvLzMyKVKJn25HKmY2wA9lYxT3FZRHxap4NMzMrVus911LKGbP9PZ8s/NgDGAr8Bdg1x3aZma2nob47tmUNI+xe/DllAzujldPNzHJR6TfIqq3dr+umBA4j82iMmVlr6nwl87LGbP+p6GMXYG/g9dxaZGbWgnqfb1pOz7Z30X4D2RjuHfk0x8ysZZ26Z5teZugdET+sUnvMzFrUaXu2krpFRIOk/arZIDOzlnTmnu0TZOOzT0uaBtwOrCocLMqWY2aWu07bsy3SA3iXbM2xwnzbABxszaxqGju6AZ9RW8F2QJqJ8ByfBNmCen+Zw8zqTFMnHkboCvSi5Qw5DrZmVlWdeRjhjYiYWLWWmJm1oTMPI9R5p93MOpPOPIzwqUS6ZmYdpd57tq0mDy+1xIOZWTU1ESW3UiRdL2mppOeKyi6UtETS02k7vOjY+ZIWSfqLpEOLysemskWSziun/V7K3MzqQmMZWxluAMa2UH55ROyVtnsBJO0CnECWTnYscJWkrunN2l8DhwG7ACemc9vU7qxfZmYdoRKzESLiUUlDyjx9HDA1Ij4CXpK0CNgnHVsUES8CSJqazl3Q1s3cszWzutCgKLl9BmdJmpeGGfqmskHAa0XnLE5lrZW3ycHWzOpCOQs+SpogaU7RNqGMW18N7AjsBbwB/DKP9nsYwczqQkMZD8AiYhIwqT33jYi3CvuSrgF+lz4uAQYXnbp9KqON8lbl2rOVtJOkGYUnf5L2kPTTPOs0s84pr6XMJW1b9PEYshQFANOAEyRtKmkoMJwsQddsYLikoZK6kz1Em1aqnrx7ttcAPwL+CyAi5km6Gfh5zvWaWSdTTs+2FEm3AAcCW0taDFwAHChpL7J4/TLwPYCImC/pNrIHXw3AmRHRmO5zFnA/WVqD6yNifqm68w62PSPiCa2/3ntDznWaWSdUiYQsEXFiC8XXtXH+JcAlLZTfC9zbnrrzDrbvSNqR9P9J0nFkA9BmZu3SWOf5r/IOtmeSDVbvLGkJ8BJwUs511rwuXbrw+Kw/8PqSNxl3zHiuu/ZyRn95FO+vWAnAqaedwzPPzOfII8dw0YU/oqkpaGho4NxzL+BP/z27g1tvrfmooZFTb53Fx41NNDYFhwzfhtP324mpT73MzXNf5rX3VvPQ6YfQt2d3ACKC//3wAv700tv06NaVi8buwRcGbgnAGys+ZOIDz/LWyg8BceWxI9huy54d+N11vM6c9asS+kbEIZI2B7pExEpJRwCv5FxvTTv7B6fx/PMvsEXvT9bS/PH5P+fOO3+/3nkPPTSTe+55AIDdd/8Ct9z8n+y2+wFVbauVr3vXLkz6+kh6du/G2sYmvjP1z+w3tD97bdeX0cMGcNptj693/syX3ubV5au5+zsH8Owb7/GLB5/jNydlq1D96x+e4bSROzJqSH9Wf9xAs6G4jVK992zznmd7jaTdImJVCrQnAP+ac501bdCgbTn8sIO5/vpbSp67atXqdfub9+xJRH3/snV2kujZPeu/NDQFDU2BJHYeuGWLvdI//vUtjthlEJLYY7u+rPyogbc/WMNf311JY1Mwakh/AHp278Zmm3St6vdSiyqRG6Ej5R1sjwOmSNpZ0nfJhhXG5FxnTbvslxdx3vk/p6lp/X8UXTzxx8x9cjq//PcL6d69+7rycePG8tyzf2Ta3ZP57nfPrXZzrZ0am4JvTHmMg69+kFGf25rdt+3T6rlLP1jDNr17rPs8sHcPln6whleXraJ3j26ce/eTnDBlJpf/cSGNTbUdSKqhkSi51bJcg216d/gEsvXKvgaMiYj3Wzu/+O2PpqZVrZ1Wt756+CEsXfoOc596dr3yn/z039h1t9GM+tJX6duvD//8ozPWHbv77vvYbfcD+Npxp3LRhT+qdpOtnbp2Ebee/GXun/AVnnvzPRa9s7Ld92iI4KnFyznngC9w47f2ZfH7q5k2f3EOra0vTWVstSyXYCvp2fSe8Tzg/wH9gKHA46msRRExKSJGRMSILl02z6NpHWrffUdw5BFjWPQ/s7jpxqs46KD9mHzDFbz55lIAPv74YyZPvpW/HfHFT1372MzHGTp0B7baqu+njlnt6d1jE0YM3or/funtVs8Z0KsHb65cs+7zWyvXMKBXDwb26sFOA7Zg+z496dalCwd9fhuef6vVPspGI8r4r5bl1bM9AjiyaBtJNnxQ+LxR+slPL2XIsBF8fqdRnPStM3j44T8x/pSz2WabAevOOeqoscxf8DwAO+44ZF35F/fajU037c677y6vdrOtTMtWf8TKNWsBWLO2kcdfeYch/VrvNByw40B+t2AJEcG815fTa9Nu9O/Vg1236cPKj9aybPVHAMx+9R2GbdWrKt9DLWuIKLnVslxmI0TEerMNJA0gWxLdWvCbyVeydf9+SOKZZ+ZzxplZLuJjjzmcb33rONaubWDNh2v45kmnd3BLrS3vrPqIn/1hHk0RNEXwd3+zLaN3HMjNc19m8uwXeXfVRxw/5TH2H9qfCw7dg/2H9mfmi0s56ro/0mOTLlx46B5ANhTxT6N35vu3P0EQfGHglhy7xw4d/N11vNoOpaUpzyfcko4iy6CzHbAU+BywMCJ2LXVtt+6D6v3/rZVpxZXHd3QTrEp6Trh8g+ewnfi5o0vGhFteuatm58jlPRvhYmAU8D8RMZRsXbNZOddpZp2QH5C1bW1EvAt0kdQlIh4GRuRcp5l1Qo00ldxqWd5vkL0nqRfwKHCTpKVA55vTZWa5q+1QWlpeU78Ko/njgNXAOcB9wF/ZiGcjmNmGa4ymklsty6tnexewd0SsknRHRHwNmJxTXWa2EajtUFpaXsG2+IngsJzqMLONSK2PyZaSV7CNVvbNzDZIvSdiyms2wp6SVkhaCeyR9ldIWilpRU51mlknVolENGmp8qWFdRFTWT9J0yW9kL72TeWSdIWkRSn9wN5F14xP578gaXw57c8l2EZE14jYIiJ6R0S3tF/4vEUedZpZ51ahFIs3AGOblZ0HzIiI4cCM9BngMLJFHocDE8iWPEdSP7K1y0YC+wAXFAJ0W/KeZ2tmVhGVmI0QEY8Cy5oVj+OTB/iTgaOLyqdEZhbQJ63EeygwPSKWRcRyYDqfDuCf4mBrZnWhnKxfxWla0zahjFsPjIjC2ohvAgPT/iDgtaLzFqey1srblPdLDWZmFdFYxgOyiJhEtu7hBomIkJTLkzj3bM2sLuS4LM5baXiA9HVpKl8CDC46b/tU1lp5mxxszawu5PgG2TSgMKNgPHB3UfnJaVbCKOD9NNxwPzBGUt/0YGxMKmuThxHMrC5UYkFHSbcABwJbS1pMNqvgUuA2SaeSrfxdyPl5L3A4sIgs7cC3ASJimaSLgdnpvIkR0fyh26c42JpZXajEsjcRcWIrhw5u4dwgW6S2pftcD1zfnrodbM2sLtR6oplSHGzNrC7U++u6DrZmVheciMbMrAqa3LM1M8ufx2zNzKqgErMROpKDrZnVBfdszcyqwGO2ZmZV0BSNHd2Ez8TB1szqQiVe1+1IDrZmVhc8ZmtmVgV+g8zMrArcszUzqwL3bM3MqsA9WzOzKvA8WzOzKqj3YQSvQWZmdaFSa5BJelnSs5KeljQnlfWTNF3SC+lr31QuSVdIWiRpnqS9N7T9DrZmVheaIkpu7XBQROwVESPS5/OAGRExHJiRPgMcBgxP2wTg6g1tv4OtmdWFHFfXBRgHTE77k4Gji8qnRGYW0Kew7Hl7OdiaWV2IiJKbpAmS5hRtE1q6FfCApCeLjg9My5QDvAkMTPuDgNeKrl2cytrND8jMrC40ldFzjYhJwKQSp+0fEUskDQCmS3q+2T1CUsWfxrlna2Z1oZyebZn3WZK+LgV+C+wDvFUYHkhfl6bTlwCDiy7fPpW1W832bBs+XqKObkNHkDQh/e1snZx/1u2ztgIxQdLmQJeIWJn2xwATgWnAeODS9PXudMk04CxJU4GRwPtFww3tq7ve5651NpLmFD0htU7MP+vqkzSMrDcLWWfz5oi4RNJWwG3ADsArwPERsUySgCuBscBq4NsRMWeD6nawrS3+A7jx8M964+IxWzOzKnCwrT0ew9t4+Ge9EfEwgplZFbhna2ZWBQ62ZmZV4GBbJZIaU5ah5yTdI6nPBt7nFElXVrp9VhlFP+fCNqSV84ZIeq66rbOO5GBbPR+mLEO7AcuAMzu6QZaLws+5sL3c0Q2y2uBg2zH+TEpmIWlHSfelpBiPSdo5lR8p6XFJT0l6UNLANu9oNUlSL0kzJM1NOVTHtXDOsPRz/tvWfh+s/tXs67qdlaSuwMHAdaloEvD9iHhB0kjgKuArwExgVEqKcRrwz8C5HdFma5fNJD2d9l8Cvg4cExErJG0NzJI0rXCypL8BpgKnRMQzkmbQ8u+D1TkH2+op/CEcBCwkyzbUC9gXuD17KxCATdPX7YFbU1KM7mR/cK32fRgRexU+SNoE+IWk0UAT2c+/8K+U/mTv4B8bEQtK/D5YnXOwrZ4PI2IvST2B+8nGbG8A3iv+w1nkP4DLImKapAOBC6vVUKuok8iC6v+KiLWSXgZ6pGPvA68C+wMLyIb1Wvt9sDrnMdsqi4jVwNlkQwKrgZckfR3WrXe0Zzp1Sz5J5Ta+6g21StkSWJoC7UHA54qOfQwcA5ws6ZsRsYLWfx+szjnYdoCIeAqYB5xI1vM5VdIzwHyyZTgg68neLulJ4J2OaKdVxE3ACEnPAicDzRNVrwKOAM6RdBSt/z5YnfPrumZmVeCerZlZFTjYmplVgYOtmVkVONiamVWBg62ZWRU42G4kmmUduz29XLGh97pB0nFp/1pJu7Rx7oGS9t2AOl5Or7eWVd7snA/aWdeFkn7Y3jaatYeD7cajOOvYx8D3iw9K2qC3CSPitIhY0MYpB5K9gmq2UXOw3Tg9Bnw+9TofS4lRFkjqKunfJc2WNE/S92Ddm0xXSvqLpAeBAYUbSXpE0oi0PzZlt3omZboaQhbUz0m96i9L6i/pjlTHbEn7pWu3kvSApPmSrgVECZLuStmx5kua0OzY5al8hqT+qaxkRi1JZ0takL7/qRv2v9esBRHhbSPYgA/S125kyU9OJ+t1rgKGpmMTgJ+m/U2BOcBQ4FhgOtAV2A54DzgunfcIMILs/f/Xiu7VL329EPhhUTtuBvZP+zsAC9P+FcDP0v5XgQC2buH7eLlQXlTHZsBzwFbpcwAnpf2fAVem/RnA8LQ/EnioeRuB14FN036fjv65ees8mxPRbDyKU/89RpbicV/giYgoZBQbA+xRGI8le69/ODAauCUiGoHXJT3Uwv1HAY8W7hURy1ppxyHALkVZrbZI2a5GkwV1IuL3kpaX8T2dLemYtD84tfVdsuxat6byG4E725FRax5wk6S7gLvKaINZWRxsNx7rpf4DSEFnVXER8IOIuL/ZeYdXsB1dyPL0rmmhLWVLmdAOAb4UEaslPcIn2bSaC8rPqPVVssB/JPATSbtHREO7GmfWAo/ZWrH7gdNTDlYk7SRpc+BR4BtpTHdb4KAWrp0FjJY0NF3bL5WvBHoXnfcA8IPCB0mF4Pco8M1UdhjQt0RbtwSWp0C7M1nPuqALUOidfxOYGWVk1JLUBRgcEQ8DP0519CrRDrOyONhasWvJ8qrOVbYY4X+R/evnt8AL6dgUsmV91hMRb5ON+d6ZMlYV/hl/D3BM4QEZWXrJEekB1AI+mRVxEVmwnk82nPBqibbeB3STtBC4lCzYF6wC9knfw1eAiam8VEatrsCNKUPXU8AVEfFeiXaYlcVZv8zMqsA9WzOzKnCwNTOrAgdbM7MqcLA1M6sCB1szsypwsDUzqwIHWzOzKvj/oyh+TG4mPfkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reporte de precision y recall:**\n",
        "\n",
        "Recordar que el vector `y` viene de la columna del dataset `is_fake`, que es True (1) cuando la noticia es falsa y es False (0) cuando la noticia es clasificada como real."
      ],
      "metadata": {
        "id": "APl0HGG3SDA8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Classification Report (only text):\\n\\n',classification_report(ytest['is_fake'], ypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdoGvwrMIjzv",
        "outputId": "64be153f-bb74-4c3a-a328-10aba0cb18eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report (only text):\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.90      0.91      0.91      4261\n",
            "        True       0.89      0.88      0.89      3559\n",
            "\n",
            "    accuracy                           0.90      7820\n",
            "   macro avg       0.90      0.90      0.90      7820\n",
            "weighted avg       0.90      0.90      0.90      7820\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Classification Report (text + title):\\n\\n',classification_report(y2test['is_fake'], y2pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxsZapv_be3X",
        "outputId": "092b87a3-5783-40fc-d0dd-876ab3bc456e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report (text + title):\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.89      0.89      0.89      4261\n",
            "        True       0.87      0.87      0.87      3559\n",
            "\n",
            "    accuracy                           0.88      7820\n",
            "   macro avg       0.88      0.88      0.88      7820\n",
            "weighted avg       0.88      0.88      0.88      7820\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Noticias mal clasificadas por temática:**\n",
        "\n",
        "A continuación se muestra un gráfico de torta que muestra las noticias mal clasificadas (solo considerando su texto), separadas por temáticas:"
      ],
      "metadata": {
        "id": "S4xte_xyRxFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colorsitos = plt.get_cmap('Set2').colors\n",
        "print(colorsitos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZFKk5ZOLEPZ",
        "outputId": "d000654f-176f-4a18-af95-fc760a758106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "((0.4, 0.7607843137254902, 0.6470588235294118), (0.9882352941176471, 0.5529411764705883, 0.3843137254901961), (0.5529411764705883, 0.6274509803921569, 0.796078431372549), (0.9058823529411765, 0.5411764705882353, 0.7647058823529411), (0.6509803921568628, 0.8470588235294118, 0.32941176470588235), (1.0, 0.8509803921568627, 0.1843137254901961), (0.8980392156862745, 0.7686274509803922, 0.5803921568627451), (0.7019607843137254, 0.7019607843137254, 0.7019607843137254))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = ytest.copy()\n",
        "results['pred'] = ypred\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BGy_O9VMMgIN",
        "outputId": "2cdbac2d-e75d-4e18-98a5-f1976381ef28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>is_fake</th>\n",
              "      <th>subject</th>\n",
              "      <th>pred</th>\n",
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              "  </thead>\n",
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              "      <th>9475</th>\n",
              "      <td>True</td>\n",
              "      <td>US_News</td>\n",
              "      <td>True</td>\n",
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              "      <td>politicsNews</td>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
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              "      <td>left-news</td>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
              "      <td>Middle-east</td>\n",
              "      <td>False</td>\n",
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              "\n",
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            "text/plain": [
              "       is_fake       subject   pred\n",
              "9475      True       US_News   True\n",
              "18140    False  politicsNews  False\n",
              "6115      True          News   True\n",
              "13852     True     left-news  False\n",
              "18781     True   Middle-east  False"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_errors_nb = results[results['is_fake'] != results['pred']]"
      ],
      "metadata": {
        "id": "38sqMrtpPi6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb_errors = df_errors_nb.groupby('subject').size()\n",
        "nb_errors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPawdI_xPjyK",
        "outputId": "7b1cf3e6-86e2-4b64-df38-d44b62a1665e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "subject\n",
              "Government News     64\n",
              "Middle-east         23\n",
              "News                71\n",
              "US_News             22\n",
              "left-news           85\n",
              "politicsNews       395\n",
              "worldnews          133\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(nb_errors, labels = nb_errors.index.values.tolist(), autopct='%1.0f%%', colors=colorsitos, shadow=True)\n",
        "plt.title('Noticias mal clasificadas (Naive Bayes) por temática')\n",
        "plt.savefig(\"pie-NB-mal-clasificadas.pdf\", bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "GY8dDlzTPuIV",
        "outputId": "fbdf5ef1-0e36-4b69-c620-90e74de850e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Y ahora se realiza el mismo procedimiento pero considerando los textos sumados a los títulos de las noticias:"
      ],
      "metadata": {
        "id": "hmP5TorKS_iu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results2 = y2test.copy()\n",
        "results2['pred'] = y2pred\n",
        "results2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gEij5c2uTAK_",
        "outputId": "19ec03e9-10ee-4add-f22f-ea130a2e32c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6cb290dd-7274-4326-b5f8-f68aae4d6ec1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>is_fake</th>\n",
              "      <th>subject</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9475</th>\n",
              "      <td>True</td>\n",
              "      <td>US_News</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18140</th>\n",
              "      <td>False</td>\n",
              "      <td>politicsNews</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6115</th>\n",
              "      <td>True</td>\n",
              "      <td>News</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13852</th>\n",
              "      <td>True</td>\n",
              "      <td>left-news</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18781</th>\n",
              "      <td>True</td>\n",
              "      <td>Middle-east</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cb290dd-7274-4326-b5f8-f68aae4d6ec1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6cb290dd-7274-4326-b5f8-f68aae4d6ec1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6cb290dd-7274-4326-b5f8-f68aae4d6ec1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       is_fake       subject   pred\n",
              "9475      True       US_News   True\n",
              "18140    False  politicsNews  False\n",
              "6115      True          News   True\n",
              "13852     True     left-news  False\n",
              "18781     True   Middle-east  False"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_errors_nb2 = results2[results2['is_fake'] != results2['pred']]"
      ],
      "metadata": {
        "id": "vHQYyk7rTEgf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb2_errors = df_errors_nb2.groupby('subject').size()\n",
        "nb2_errors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4j2ngkPmTHaS",
        "outputId": "807a1bc0-fa7d-4d14-f74c-f8a1dee60679"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "subject\n",
              "Government News     74\n",
              "Middle-east         21\n",
              "News                60\n",
              "US_News             20\n",
              "left-news           91\n",
              "politicsNews       508\n",
              "worldnews          130\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(nb2_errors, labels = nb2_errors.index.values.tolist(), autopct='%1.0f%%', colors=colorsitos, shadow=True)\n",
        "plt.title('Noticias mal clasificadas (Naive Bayes) por temática (title + text)')\n",
        "plt.savefig(\"pie-NB-mal-clasificadas-con-titulo.pdf\", bbox_inches='tight')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "A97iyauQTLhJ",
        "outputId": "23a6121a-d78e-4a47-becf-4b698f1911e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Decision Tree**\n",
        "\n",
        "Los **árboles de decisión** son una técnica de aprendizaje supervisado en la que se recrea un árbol cuyos nodos internos representan atributos y sus conexiones/aristas representan valores de esos atributos. Las hojas de este árbol corresponderán a las clases que tiene nuestro problema. El árbol solo puede tomar decisiones discretas, por lo que en caso de tener atributos continuos se deberá discretizar el intervalo. \n",
        "\n",
        "Por otro lado, el gran problema de los árboles de decisión es que tienden fuertemente al *overfitting*. En teoría, si dejáramos al árbol crear la mejor estructura que pueda, lo que haría sería dividir tanto los datos que en cada nodo hoja quedaría 1 solo dato en el peor caso, con un accuracy de 100% ya que la estructura se adaptó para clasificar bien todos los datos con los que se le entrenó. Sin embargo, es difícil que esa estructura aplique para otros datos. Por esta razón se setea un segundo hiperparámetro llamado ``max_depth``, que intenta evitar el overfitting poniéndole limites a la clasificación del árbol en profundidad. "
      ],
      "metadata": {
        "id": "QSYxS5pgIu8k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación se aplica el método de clasificación de Decision Tree considerando **sólo los textos** de las noticias:"
      ],
      "metadata": {
        "id": "qtzTEWUfXor7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dtree = DecisionTreeClassifier(max_depth=10, random_state=0).fit(Xtrain, ytrain['is_fake'])\n",
        "print('\\nAccuracy score :', dtree.score(Xtest, ytest['is_fake']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh5CRSHaIp53",
        "outputId": "af0263ac-28d8-48b6-80bd-74f6333f99ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score : 0.8971867007672635\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "y ahora se aplica Decision Tree considerando tanto los **títulos** como los **textos** de las noticias:"
      ],
      "metadata": {
        "id": "nKW0JSTfYB_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dtree2 = DecisionTreeClassifier(max_depth=10, random_state=0).fit(X2train, y2train['is_fake'])\n",
        "print('\\nAccuracy score :', dtree2.score(X2test, y2test['is_fake']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nwAnAtmbpzA",
        "outputId": "8228ec3c-f921-4432-8318-60c4fcb9f458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score : 0.8920716112531969\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos observar que, pese a que los accuracys obtenidos son bastante similares, al agregar los títulos de las noticias el accuracy baja de un **89.71%** a un **89.2%**. También podemos apreciar que los accuracys entre Naive Bayes y Decision Tree son muy similares, y aunque Naive Bayes alcanza el mayor accuracy con un 89.85% al solo considerar los textos, al considerar los títulos más el texto, Decision Tree tiene un mejor accuracy que Naive Bayes."
      ],
      "metadata": {
        "id": "BRNGIoGTYWdn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analicemos un poco más los resultados del Decision Tree:"
      ],
      "metadata": {
        "id": "gAgI1wCCY_eS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = dtree.predict(Xtest)\n",
        "y2pred = dtree2.predict(X2test)\n",
        "cm = confusion_matrix(ytest['is_fake'], ypred)\n",
        "cm2 = confusion_matrix(y2test['is_fake'], y2pred)"
      ],
      "metadata": {
        "id": "HxlfbC40rMPX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(2, figsize=(5,7))\n",
        "categorias = ['Real', 'Fake'] #0: Real, 1: Fake\n",
        "\n",
        "sns.heatmap(cm, annot=True, ax = ax1, fmt='g');\n",
        "ax1.set_xlabel('Predicted labels');\n",
        "ax1.set_ylabel('True labels'); \n",
        "ax1.set_title('Confusion Matrix Decision Tree \\n (only text)'); \n",
        "ax1.xaxis.set_ticklabels(categorias); \n",
        "ax1.yaxis.set_ticklabels(categorias);\n",
        "\n",
        "sns.heatmap(cm2, annot=True, ax = ax2, fmt='g');\n",
        "ax2.set_xlabel('Predicted labels');\n",
        "ax2.set_ylabel('True labels'); \n",
        "ax2.set_title('Confusion Matrix Decision Tree \\n (title + text)'); \n",
        "ax2.xaxis.set_ticklabels(categorias); \n",
        "ax2.yaxis.set_ticklabels(categorias);\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "fig.savefig(\"CM-DT.pdf\", bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "J1J28fozi7zO",
        "outputId": "bfcd87e0-3210-423d-d060-add22a27e94f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GxTz55PNcMeh6AK4cdAG/+90BlJWVcfvtw7j5lpWj9PXYfmveenM4vz/ujzz55PO1Vf26KQtDLNamXAfbtmZ2l6SzzWw0MFrSmLR7rUUaNizh7puuoWnTJiwvLeWEM85jt549uPK6W7jpmkv5VeeNePjJ57j93ocYfMm5XHfznRzUZ28O7rcv730wnn/9916uufR8dtx+a54YegsA8xcspO+RJ7PLjtulObqrLcuWLWOf3keyePESGjRowBujnuLFF19n8803oVOnDnTfcnfMjPbt267Yp6ioiL9ffTEjR46uxZrXYVm49UtSY+ANoBEh/j1uZpdJ6gI8DLQFPgCON7Nf4sSPw4DtgdnAUWY2KZZ1IXAKUAacZWbVztSQ62lxlsf/p0vaX9K2QJscH7NOkUTTpk0AKC0tpbS0FEkIWLx4CQALFy2mfbvwS/fNdz+w4/bbALDjdlvz+pvvrFLmy6+/yW49e9CkceNVtrm6I/H5lpQ0oEFJCWbG6aedwFWDb8Ri4Jg1a/aK/GcOPJknn3qemUlpLklpWfolvWXAXma2NbAN0CdOd3MtcKOZbQLMJQRR4v9zY/qNMR+StgCOBroTpjC/Nc7+UKVcB9urJLUEzgXOA+4EzsnxMeucsrIyftd/ILsfcAw777AtW3XfnCv+9mfOOO9S9j7kOJ596VVOPf4IADbr1pVXRv8PgFdGv83iJUuZN39BhfJeeOUN+u7bK9+n4WqoqKiIsWNeZvrUj3n11Td4f8w4unbtzJFHHMS774zgueH3sckmXQDo0GF9Djm4D/+9fVgt17oOs/L0S7oigkVxtSQuBuwFPB7ThxLmIQM4OK4Tt+8d5yk7GHjYzJaZ2XeEaXMSk0SmlNNga2bPmdl8M5tgZnua2fZmNryq/Mlzvt857KFcVi2viouLeWLoLbz61H188tlXfP3tJIY98hS3XTeIV5++n0P69eYfN90BwHkDT2XsuE84/MSBjB3/Ceu1b0tR0cqPadZPc/j62+/47U7b19bpuAyVl5fTY4febNylBzv02Jbu3TejUaOG/PzzMnru3I87736QO4eEftwbrr+CCy+6ekWL163KSsvSLpmQVCxpPDATGAl8A8wzs0Sn8BSgY3zdEZgMELfPJ3Q1rEhPsU9Kub4bYVPgNmA9M9tS0lbAQWZ2Var8yXO+L//p27Xup65F82bsuN1WvPnOWL6c+C1bdd8cgL57785p514CwLrt2/Lvv/8fAEuWLOWVUW/RonmzFWW8+Nob7L37LpQ08If/CsX8+QsYNfp/7Ne7F1OmTuepp0cA8PTTL3DXHTcAsP12W/HA/bcC0K5dG/r22YvS0lKGD6+2G7B+yeBuBEkDgAFJSUNiXFnBzMqAbeIsu08Bm2ezmlXJdTfCHcCFxL7bONna0Tk+Zp0yZ+48FiwM31p+XraMd8aMo2vnDVm0eAmTfpgCwNtjxtF1440AmDtvPuXxscQ77nuEQ/fvXaG8F0aOot8+vfJ3Am61tGvXhpYtWwDQuHFj9tl7d7788huGD3+RXnuEO0j22H1nvvr6WwC6bbYzm2zak0027ckTTz7PmWdd5IG2srKytIuZDTGzHknLkKqKM7N5wOvAzkArSYkWTCdganw9FdgQIG5vSbhQtiI9xT4p5bp51NTM3g9dHCsU9v0bNTRr9lwuvuo6ysrLsXJjv712o9dvd+LyC87inIsHoyLRonkzrrwwdGWPGfcx//rvvUhi+6235JJz/7iirKnTf2TGzJ/ose1vaut0XIY22GA97r7rXxQXF1FUVMTjjz/L8yNe4a3/vc99Q2/m7LP/wOJFSzjt9PNru6qFIwtjI8R7/5eb2TxJTYB9CRe9XgcOJ9yR0B94Ju4yPK6/E7e/ZmYmaTjwoKQbgA5AN+D9ao+dyz4iSS8AZwKPmdl2kg4HTjGzvun2XRu7EVxqTTrsVttVcHlS+stUpc+V2uL/OzJtTFjnykerLT92ZQ4Fignf7B81s0GSuhICbRtgHHCcmS2Lt4rdR5j+fA5wtJl9G8u6GDiZ0ID8s5m9UN2xc92yHUjog91c0lTgO+DYHB/TObc2ysITZLErc9sU6d+S4m4CM/sZOKKKsgYDgzM9dk6DbTyBfSStQ/grsoTQZ/t9Lo/rnFv7ZHq3QV2VkwtkklpIulDSzZL2JQTZ/oR70Y7MxTGdc2u5cku/1GG5atneR3gK4x3gD8DFgIBDzWx8jo7pnFub+YSPKXU1s98ASLoTmA5sFPs/nHOuxnw829QSYyJgZmWSpnigdc6tEQ+2KW0tKfFAv4AmcV2Ex5Nb5Oi4zrm1VYFfIMvVTA3Vjn7jnHM15i1b55zLPSvz2XWdcy73vGXrnHO5Z6XesnXOudwr7FjrwdY5VxgKvWVbo8d1JbWOo+Y451x+lWew1GFpW7aSRgEHxbwfADMl/c/M/pLjujnn3ApWWtgXyDJp2bY0swXAYcAwM9sJ2Ce31XLOuYqs3NIudVkmwbaBpA0Io3U9l+P6OOdcSlaafqnLMgm2g4CXgIlmNiaOaP51bqvlnHOVZKHPVtKGkl6X9JmkTyWdHdMvlzRV0vi49Eva50JJEyV9KWm/pPQ+MW2ipL+lO3baPlszewx4LGn9W+B36U/LOeeyJ0st11LgXDP7UFJz4ANJI+O2G83suuTMkrYgTHjQnTDX2Ctx1nCAWwhzmE0BxkgabmafVXXgKoOtpP8AVXaCmNlZ6c/LOeeyw7Jwt4GZTScM+YqZLZT0OdCxml0OBh42s2XAd5ImsnL6nIlJ85E9HPPWPNgCYzM/Beecy63yDFq2kgYAA5KShlQ1nbmkzoT5yN4DfgucKekEQuw718zmEgLxu0m7TWFlcJ5cKX2n6upWZbA1s6GVKtbUzJZUV5hzzuWMpZ+YNwbWlME1maRmwBOEWXEXSLoNuJLwbf5K4HrCzLlZk/YCmaSdJX0GfBHXt5Z0azYr4Zxz6Vh5+iUTkkoIgfYBM3sSwMx+NLMyMysH7mBlV8FUYMOk3TvFtKrSq5TJ3Qj/AvYDZsdKfQTsnsF+zjmXNeWlSrukI0nAXcDnZnZDUvoGSdkOBSbE18OBoyU1ktQF6Aa8D4wBuknqIqkh4SLa8OqOndHYCGY2OdRxhcIeMt05V3Asg26EDPwWOB74RFJi8tmLgGMkbUPoRpgEnBaOaZ9KepRw4asUGGhmZQCSziTcFlsM3G1mn1Z34EyC7WRJuwAWm99nA5/X7Pycc27NZNJyTcfM3iJMz1XZiGr2GQwMTpE+orr9KsukG+F0YCDhCtw0YJu47pxzeWOWfqnLMnmo4Sfg2DzUxTnnqlReWqNBCuucTO5G6CrpWUmzJM2U9Ex8ZNc55/Km0Fu2mfypeBB4FNiA8LjaY8BDuayUc85VVl5WlHapyzKpXVMzu8/MSuNyP9A41xVzzrlk2brPtrZUNzZCm/jyhTiizcOE2yKOogZX4JxzLhvKyut2yzWd6i6QfUAIronbJE5L2mbAhbmqlHPOVWblWbnPttZUNzZCl3xWxDnnqlNetpYG22SStgS2IKmv1syG5apSzjlXWXl2niCrNZlM+HgZ0IsQbEcAfYG3AA+2zrm8KS/wboRMepwPB/YGZpjZScDWQMuc1so55yopN6Vd6rJMuhGWmlm5pFJJLYCZVBxazDnncq7QW7aZBNuxkloRxnj8AFgEvJPTWgEtNtwz14dwdcSCGw+t7Sq4AlDXW67pZDI2wh/jy/9KehFoYWYf57ZazjlX0Vp7n62k7arbZmYf5qZKzjm3qjo+9EFa1bVsr69mmwF7ZbkuzjlXpbW2G8HMvNPUOVdnlGUh2ErakHDb6nqERuMQM/t3HJ7gEaAzYaaGI81sbpxG599AP2AJcGLiW72k/sAlseirKk+SW1lhd4I45+oNQ2mXDJQSpinfAugJDJS0BfA34FUz6wa8GtchPFfQLS4DgNtgxdgxlxGmL98RuExS6+oO7MHWOVcQSk1pl3TMbHqiZWpmCwlTfHUEDgYSLdOhwCHx9cHAMAveBVrFySH3A0aa2RwzmwuMBPpUd2wPts65gpBJy1bSAEljk5YBVZUnqTOwLfAesJ6ZTY+bZhC6GSAE4slJu02JaVWlVymTx3VFmBanq5kNkrQRsL6ZvZ9uX+ecy5ayDLoJzGwIMCRdPknNgCeAP5vZguTZw83MJGX95odMWra3AjsDx8T1hcAt2a6Ic85VpzyDJRNxlvAngAfM7MmY/GPsHiD+PzOmT6XiE7OdYlpV6VXKJNjuZGYDgZ8BYv9Ewwz2c865rClDaZd04jf1u4DPzeyGpE3Dgf7xdX/gmaT0ExT0BObH7oaXgN6SWscLY71jWpUyeVx3uaRi4j3FktqT+R8R55zLiiwNjfBb4HjgE0njY9pFwDXAo5JOAb4HjozbRhBu+5pIuPXrJAAzmyPpSmBMzDfIzOZUd+BMgu1NwFPAupIGE0YBu6T6XZxzLrsyabmmY2ZvQZUF7Z0ivwEDqyjrbuDuTI+dydgID0j6IFZEwCFm9nmmB3DOuWwo9K/TmdyNsBGh+fxscpqZ/ZDLijnnXLIyraWP6yZ5npUTPzYGugBfAt1zWC/nnKtgrW/ZmtlvktfjaGB/rCK7c87lRGk9aNlWYGYfStopF5VxzrmqrM1DLAIg6S9Jq0XAdsC0nNXIOedSKC3shm1GLdvmSa9LCX24T+SmOs45l9pa3bKNDzM0N7Pz8lQf55xLaa1t2UpqYGalkn6bzwo551wqa/PdCO8T+mfHSxoOPAYsTmxMGsDBOedyrsBnxcmoz7YxMJsw51jiflsDPNg65/KmtLYrsIaqC7brxjsRJrAyyCYUel+1c67AFHrQqS7YFgPNSD1oQ6Gft3OuwKy1F8iA6WY2KG81cc65aqzNF8gK/O+Ic25tUlbgEam6mRpWGdvROedqSzamxZF0t6SZkiYkpV0uaaqk8XHpl7TtQkkTJX0pab+k9D4xbaKkv1U+TipVBtt0o44751w+lWFplwzcS+opx280s23iMgJA0hbA0YQRDvsAt0oqjg973QL0BbYAjol5q1XjgWicc642ZKPP1szeiFOYZ+Jg4GEzWwZ8J2kisGPcNtHMvgWQ9HDM+1l1hWUy4aNzztW6sgwWSQMkjU1aBmRY/JmSPo7dDK1jWkdgclKeKTGtqvRqebB1zhWEcqVfzGyImfVIWoZkUPRtwK+AbYDpwPW5qL93IzjnCkKGfbI1ZmY/Jl5LugN4Lq5OBTZMytopplFNepW8ZeucKwjZuBshFUkbJK0eSnhqFmA4cLSkRpK6AN0IY8aMAbpJ6iKpIeEi2vB0x/GWrXOuIGSjZSvpIaAX0E7SFOAyoJekbQhPxk4CTgMws08lPUq48FUKDDSzsljOmcBLhCdt7zazT9MdO6fBVtKmhP6Q9cxsS0lbAQeZ2VW5PK5zbu2TpbsRjkmRfFc1+QcDg1OkjwBG1OTYue5GuAO4EFgOYGYfE5rczjlXI1m6z7bW5LoboamZva+Ks2IW+khpzrlasDaPjZANP0n6FXGUMEmHE26tcM65GqnrLdd0ch1sBwJDgM0lTQW+A47N8THrvKKiIv73v+eYNm0Gv/vdySvSr7/+ck444Ujatw9P/jVs2JC77rqBbbf9DXPmzOW4487khx+m1Fa1XRrLSss45Ymx/FJWTlm5sc8m63FGz1/x8Ec/8OD4H5g8fymv/WEPWjdpCMB3cxZz2Suf8sXMBZy5yyacsF3nFWU9OP4HnpwwBQMO696RY7fduHZOqg4p92BbrdZmto+kdYAiM1so6QDg+xwft04788yT+fLLiTRv3mxF2nbb/YZWrVpWyHfiiUcxd+58ttxyD4444kAGD/4bxx9/Zr6r6zLUsLiIIYduT9OGDVheVs7Jj4/htxu3ZZsNWrF7l/ac+sTYCvlbNi7hgj024/VvZlVInzh7EU9OmMJ9R+1ESbEY+Mw4duvSno1aNc3n6dQ5hR1q83CBTNKWZrY4Btqjgf/L8THrtI4d16dPn724556HV6QVFRVx9dUXc/HFf6+Q94AD9uWBB8Ks8U8+OYJevXzuzbpMEk0bhvZLablRWm5IYvN1W9ChRZNV8rdp2pDu67WkQVHFsQO/m7OYLddvSagdu88AACAASURBVJOSYhoUFbF9x9a89s3MvJxDXVaKpV3qslwH28OBYZI2l/QHQrdC7xwfs0775z8v4+KLr6a8fGV3/xln9Of550cyY0bFX6gOHdZnypRpAJSVlbFgwULatm2Nq7vKyo2jHnyHve8cTc+N2vKb9Vum36mSX7Vdh3HT5jFv6S8sXV7GW5N+YsbCn3NQ28JiGfyry3LajWBm38bW7NPAD0BvM1taVf44aMQAgAYN2tCgQbOqshakvn33YubM2YwbN4HddusJwAYbrMthh+1P795H1XLtXDYUF4lHfr8zC5ct5y/PfcTE2YvYpG3Nfo67tmnGidt35o9Pf0jjkmI2a9+c4qICHzk7C/wCWQqSPqFiF0sbwpMW70nCzLZKtV8cNGIIQJMmGxf2O5vCzjv34IAD9qFPn140atSIFi2a88EHr7Bs2TI+/XQ0AE2bNmHChNFsueUeTJs2g06dOjB16gyKi4tp0aI5s2fPreWzcJlo3qiEHp1a8/b3P9U42AIc2r0jh3YPA0n95+2vWa9Z42xXseD4rV+pHZCjcgvapZf+g0sv/QcAu+3Wkz//eUCFuxEAZs36jC233AOA559/hWOP/R3vvfchhx3Wj9Gj3857nV3m5iz5hZJi0bxRCT+XlvHe5DmcuH3n1S6rTdOGTF+4lNe+mcmwI3dMv9NarswKu/2Vk2BrZhXuNpC0LuB/mmvo3nsf4e67b2TChNHMnTvP70So435asoxLX/6UcjPKzdi323rs3qU9D47/gaEfTGL2kl848sF32HXjdly2T3d+WryMYx9+j8W/lCKJB8b9wBPH7UKzRg04b8RHzFu6nAbF4m+9Nqd5o5LaPr1aV+i3fsly+NdC0kGEsSE7ADOBjYHPzax7un3Xxm4El9rs6w6s7Sq4PGk68ObV7nw+auND0saER75/us52buf6boQrgZ7AV2bWhTCJ5Ls5PqZzbi1UjqVd6rJcB9vlZjYbKJJUZGavAz1yfEzn3FrIB6Kp3jxJzYA3gAckzQQW5/iYzrm1UC67PPMhJy1bSRvFlwcDS4BzgBeBbwDvoHPO1Zg/QZba0wBmthh4zMxKzWyomd0UuxWcc65GsvEEWZw9d6akCUlpbSSNlPR1/L91TJekmyRNjDPvbpe0T/+Y/2tJ/TOpf66CbfIVwa45OoZzrh4ps/K0SwbuBfpUSvsb8KqZdQNejesAfQnzjnUjPNl6G4TgTJhOZydgR+CypOnPq5SrYGtVvHbOudWSjQkfzewNYE6l5IOBofH1UOCQpPRhFrwLtIqTQ+4HjDSzOWY2FxjJqgF8Fbm6QLa1pAWEFm6T+Jq4bmbWIkfHdc6tpcoyCKfJ46tEQ+IwANVZz8wSkxrMANaLrzsCk5PyTYlpVaVXK1dPkBXnolznXP2Vyd0IyeOrrOYxTFJOvo3n+j5b55zLihw+1PBj7B4g/p8Y63QqsGFSvk4xrar0anmwdc4VhCxdIEtlOJC4o6A/8ExS+gnxroSewPzY3fAS0FtS63hhrHdMq1auH2pwzrmsyMZ3e0kPAb2AdpKmEO4quAZ4VNIphCm7jozZRwD9gImE5wVOAjCzOZKuBMbEfIPMrPJFt1V4sHXOFYTSLIxoa2bHVLFp7xR5jTC7TKpy7gbursmxPdg65wpCoT+u68HWOVcQMrn1qy7zYOucKwjesnXOuTxYg7sN6gQPts65glDXBwdPx4Otc64geMvWOefyIJMhFOsyD7bOuYLgLVvnnMuDcr8bwTnncs9bts45lwfeZ+ucc3lQZmW1XYU14sHWOVcQ/Aky55zLA++zdc65PCj0uxF8pgbnXEGwDP5lQtIkSZ9IGi9pbExrI2mkpK/j/61juiTdJGmipI8lbbe69fdg65wrCFmeFmdPM9vGzHrE9b8Br5pZN+DVuA7QF+gWlwHAbatbfw+2zrmCYGZplzVwMDA0vh4KHJKUPsyCd4FWickha8qDrXOuIJSVl6ddMmTAy5I+kDQgpq0XJ3MEmAGsF193BCYn7TslptWYXyBzzhWETIZYjMFzQFLSEDMbUinbrmY2VdK6wEhJXyRvNDOTlPWrcR5snXMFIZOWawyslYNr5TxT4/8zJT0F7Aj8KGkDM5seuwlmxuxTgQ2Tdu8U02rMuxGccwUhG3cjSFpHUvPEa6A3MAEYDvSP2foDz8TXw4ET4l0JPYH5Sd0NNeItW+dcQahBn2x11gOekgQh/j1oZi9KGgM8KukU4HvgyJh/BNAPmAgsAU5a3QN7sHXOFYRsDERjZt8CW6dInw3snSLdgIFrfGA82DrnCkR5dlq2tUaFPrjD2kbSgBRXT91ayD/r+sUvkNU9A9JncWsJ/6zrEQ+2zjmXBx5snXMuDzzY1j3eh1d/+Gddj/gFMuecywNv2TrnXB54sHXOuTzwYJsnksriyPATJD0rqdVqlnOipJuzXT+XHUmfc2LpXEW+zpIm5Ld2rjZ5sM2fpXFk+C2BOWTpEUBX5yQ+58QyqbYr5OoGD7a14x3iAMSSfiXpxTiQ8ZuSNo/pB0p6T9I4Sa9IWq/aEl2dJKmZpFclfRjnvTo4RZ6u8XPeoaqfB1f4fGyEPJNUTBjw4q6YNAQ43cy+lrQTcCuwF/AW0DMOZHwq8Ffg3Nqos6uRJpLGx9ffAUcAh5rZAkntgHclDU9klrQZ8DBwopl9JOlVUv88uALnwTZ/Er+EHYHPCSPENwN2AR6LQ74BNIr/dwIeiQMZNyT84rq6b6mZbZNYkVQCXC1pd6Cc8PknvqW0J4ybepiZfZbm58EVOA+2+bPUzLaR1BR4idBney8wL/mXM8l/gBvMbLikXsDl+aqoy6pjCUF1ezNbLmkS0Dhumw/8AOwKfEbo1qvq58EVOO+zzTMzWwKcRegSWAJ8J+kIWDFHfWKszZasnH6j/yoFuULREpgZA+2ewMZJ234BDiXMBPB7M1tA1T8PrsB5sK0FZjYO+Bg4htDyOUXSR8CnhKmTIbRkH5P0AfBTbdTTZcUDQA9JnwAnAJUnF1wMHACcI+kgqv55cAXOH9d1zrk88Jatc87lgQdb55zLAw+2zjmXBx5snXMuDzzYOudcHniwrScqjTr2WHy4YnXLulfS4fH1nZK2qCZvL0m7rMYxJsXHWzNKr5RnUQ2Pdbmk82paR+dqwoNt/ZE86tgvwOnJGyWt1tOEZnaqmX1WTZZehEdQnavXPNjWT28Cm8RW55txYJTPJBVL+qekMZI+lnQarHiS6WZJX0p6BVg3UZCkUZJ6xNd94uhWH8WRrjoTgvo5sVW9m6T2kp6Ixxgj6bdx37aSXpb0qaQ7AZGGpKfj6FifShpQaduNMf1VSe1jWtoRtSSdJemzeP4Pr97b61wKZuZLPViARfH/BoTBT84gtDoXA13itgHAJfF1I2As0AU4DBgJFAMdgHnA4THfKKAH4fn/yUlltYn/Xw6cl1SPB4Fd4+uNgM/j65uAS+Pr/QED2qU4j0mJ9KRjNAEmAG3jugHHxteXAjfH168C3eLrnYDXKtcRmAY0iq9b1fbn5svas/hANPVH8tB/bxKGeNwFeN/MEiOK9Qa2SvTHEp7r7wbsDjxkZmXANEmvpSi/J/BGoiwzm1NFPfYBtkga1apFHO1qd0JQx8yelzQ3g3M6S9Kh8fWGsa6zCaNrPRLT7weerMGIWh8DD0h6Gng6gzo4lxEPtvVHhaH/AGLQWZycBPzJzF6qlK9fFutRRBin9+cUdclYHAltH2BnM1siaRQrR9OqzMh8RK39CYH/QOBiSb8xs9IaVc65FLzP1iV7CTgjjsGKpE0lrQO8ARwV+3Q3APZMse+7wO6SusR928T0hUDzpHwvA39KrEhKBL83gN/HtL5A6zR1bQnMjYF2c0LLOqEISLTOfw+8ZRmMqCWpCNjQzF4HLojHaJamHs5lxIOtS3YnYVzVDxUmI7yd8O3nKeDruG0YYVqfCsxsFqHP98k4YlXia/yzwKGJC2SE4SV7xAtQn7HyrogrCMH6U0J3wg9p6voi0EDS58A1hGCfsBjYMZ7DXsCgmJ5uRK1i4P44Qtc44CYzm5emHs5lxEf9cs65PPCWrXPO5YEHW+ecywMPts45lwcebJ1zLg882DrnXB54sHXOuTzwYOucc3ngwdY55/LAg61zzuWBB1vnnMsDD7bOOZcHHmydcy4PPNjmiaQmkp6VNF/SY2tQzrGSXs5m3WqDpBck9a/teiSL0/Z8mUG+i+LUPc5lzINtJZJ+L2mspEWSpsegsGsWij4cWI8wdcsRq1uImT1gZr2zUJ8K4nxkJumpSulbx/RRGZZzuaT70+Uzs75mNnQ16nmiwkzBi+LynaR7JG1a07JS1OlNM9ssg3xXm9mpa3q8ZDGAJ87p50rn+Gk2j+VqhwfbJJL+AvwLuJoQGDcCbmXVcU9Xx8bAV3V81P9ZwM6S2ial9Qe+ytYB4qDda/pz946ZNSMM7r0PsBT4QNKWa1zBWhIDeLN4XqcTzzEu3RP5svT+udpQ25Og1ZWF8Iu7CDiimjyNCMF4Wlz+xcrJAXsBU4BzgZnAdOCkuO0KwvThy+MxTiFMMnh/UtmdCdO3NIjrJwLfEmY6+I6VExieSJh5ILHfLsAYYH78f5ekbaOAK4H/xXJeJsUkipXq/19gYEwrBqYSJk0clZT334TJHRcAHwC7xfQ+lc7zo6R6DI71WApsEtNOjdtvA55IKv9awuSMSlHPCueflP4c8HjSek/gbcLklB8BvZK2tQHuiZ/hXODp5PcgKd8F8fwXAl8Ce8f0yp/dQYTByOfF8/p10rZJwHmEuc3mEwZVb5zmZ7HyZ5zq/ducMAnnnFi3Iyv9nF5HGID9x/iZNqnt37H6vtR6BerKEgNFKTHYVZFnEGFGgHUJs8m+DVwZt/WK+w8CSoB+wBKgddxe+Re08npnYrAF1iEEss3itg2A7vH1il/EGDTmAsfH/Y6J64lZZkcB3wCbEmagHQVcU8W59SIE212A92JaP8JUOadSMdgeB7SNxzwXmJEIIJXPK6kePwDd4z4lVAy2TQmt5xOB3YCfgE5V1LNCIEpKPxn4Mb7uSJj4sR/h29u+cb193P48Iei1jnXZI/k9iK83I/xB6ZD0+fyq8jnG93ZxPEYJ8FdgItAwbp8EvE+YlbgN8DlwepqfxQrnmOL9axnrdlJc3za+Z1vE/DcCw+PxmhNmy/h7bf+O1ffFv46s1Bb4yar/mn8sMMjMZlqYBuYKQqBLWB63LzezEYTWXdo+wCqUA1tKamJm080sVb/d/sDXZnafmZWa2UPAF4TJChPuMbOvzGwp8ChQ7YSHZvY20EbSZsAJhGlwKue538xmx2NeT2hJpTvPe83s07jP8krlLSG8jzcQZsP9k5lNSVNeZdMIwQXCH4MRZjbCzMrNbCRhWvZ+cQ61voSANzd+VqNTlFcWz2sLSSVmNsnMvkmR7yjgeTMbGc/rOsIftl2S8txkZtMszDj8LGk+gyqseP8IDYNJZnZPfD/HAU8ARyjMnDkAOMfM5pjZQkK32NGrcUyXRR5sV5oNtJNU3YzDHYDvk9a/j2kryqgUrJewGhMGmtliwi/x6cB0Sc/HSQ3T1SdRp45J6zNWoz73AWcSJnZ8qvJGSedJ+jzeWTGP0NJql6bMydVtNLP3CN0mIvxRqKmOhK/UEPrHj5A0L7EAuxK+IWwIzDGzaqdKN7OJwJ8JrdiZkh6W1CFF1gqfgZmVE851TT+DypLfv42BnSqd37HA+oRvXE0JfdiJbS/GdFeLPNiu9A6wDDikmjzTCD/oCRvFtNWxmPBLkbB+8kYze8nM9iUEiC+AOzKoT6JOU1ezTgn3AX8ktA6XJG+Ikzb+FTiS0EXSitAXmZiLvKpJ7aqd7E7SQEJLclosv6YOBd6MrycD95lZq6RlHTO7Jm5rI6lVugLN7EEz25XwHhuhL7myCp9BbFluyJp/BqtUJ+n1ZGB0pfNrZmZnELoTlhK6nRLbWlq48OZqkQfbyMzmEy4E3SLpEElNJZVI6ivpHzHbQ8AlktpLahfzp73NqQrjCbPJbiSpJXBhYoOk9SQdHKcRX0bojihPUcYIYNN4u1oDSUcBWxAuFq02M/sO2AO4OMXm5oS+6VmE2W0vBVokbf8R6FyTK+bxtq2rCF//jwf+mjTFeXX7FUvqIuk/hP7WK+Km+4EDJe0X8zSOt7Z1MrPpwAvArZJax8949xRlbyZpL0mNgJ8JASzVZ/AosL+kvRWmgD+X8Jm9nen5r4bnCJ/78bH+JZJ2kPTr2LK+A7hR0rrxXDpK2i+H9XEZ8GCbJPY//gW4hBBMJhO+Tj8ds1xF6Pv7GPgE+DCmrc6xRhIu0nxMuKKfHCCLYj2mEb4a7wGckaKM2cABhF/w2YQW4QFm9tPq1KlS2W+ZWapW+0uEr6VfEb4+/0zFr7iJBzZmS/ow3XFit839wLVm9pGZfQ1cBNwXA10qO0taRLiIOIoQ7Hcws09i3ScTbte7iJWf4/ms/Hk/ntC//gXhzpE/pzhGI8IU6T8RugHWJekPYoKZfUn4I/GfmPdA4EAz+yXdua+u2A/bm9APOy3W79pYZwh3UUwE3pW0AHiF1b924LLEpzJ3zrk88Jatc87lgQdb55zLAw+2zjmXBx5snXMuDzzY1nOSHpJU5b3Fkv4r6f+q2Z7RKF+FQtJWknJ525arpzzY1mOStgK2Bp6J6ydKeis5j5mdbmZXxu29JNX0Mdqsi0M+bpKLsszsY2CepAOr2c25GvNgW7+dBjxgdeD+v9hCvry26xE9QHhvnMsaD7b1W19gNICkXxOG4ts5Dlg9L6bfK+mq+DTbC0CHpEGtVxkrQFJPSW/H5/I/ktQrmxWW9EZ8+VGsw1Ex/QBJ4+Nx346tdiQdpTDAeIu43lfSjPgUYMqyCA9K7F3NQxXO1ZgH23oqBs8uhLFQMbPPqThodYWxA+LgOH2BabZyUOtplcrsSBi+8CrCCFznAU9IytogKGaWeLR261iHRyRtC9xNaI22BW4HhktqZGaPEB6dvUlhUPS7CEM7zkpVVjzGVMITZv7UlcsaD7b1VyKYLsximVUObZjFY6QyALjdzN4zszIL0+0sIwwgDjAQ2IvQYn3WzDIZO2IhK98j59ZYdcMJurXbvPh/c8L4BtmQGNow+eJSCfB6qsySniMMfQjQOKYlxil4y8wOqMFx+0v6U1JaQ+Lwl2Y2T2GSzb8Av8uwzOasfI+cW2MebOspM1ssKTGLw6xEcrrd0mxPDG34hwzrsCKYJi6Omdnlmeyb4riDzWxwqo1xBLGTCaO23UQYfLtKsTukIbGLxbls8G6E+m0EYUSxhB+BTpIaVpH/R6BtHBIylSqHNsxinRP16Jq0fgdwuqSdFKwjaX9JzSU1jvW6iDCNTEdJf6ymLAjvyWtmtizL9Xb1mAfb+m0IcKykxMDfrxEmLpwhaZVhGs3sC0Lr8Nt41b9Dpe3phjbMlsuBobEOR5rZWOAPwM2EOdgmEubxAvg7MNnMbovB8zjgKkndUpUV044l3JnhXNb4EIv1nKQHgUfN7Om0meuBeMvY7Wa2c23Xxa1dPNg651weeDeCc87lgQdb55zLAw+2zjmXB3X2PtvlP33rncn1RJMOu9V2FVyelP4yVelzpZZJTChp13W1y8+1OhtsnXOugvKy2q7BGvFg65wrDFZe2zVYIx5snXMFwcpKa7sKa8SDrXOuMJR7y9Y553KvbHlt12CNeLB1zhUG77N1zrnc8z5b55zLB++zdc65PPA+W+ecywPvs3XOuTzwPlvnnMsD77N1zrncs3Lvs3XOudwr8Jatj2frnCsMZcvTL2nEGZ/fl/SRpE8lXRHT75X0naTxcdkmpkvSTZImSvpY0nZJZfWX9HVc+qc7trdsnXOFITt3IywD9jKzRZJKgLckvRC3nW9mj1fK3xfoFpedgNuAnSS1AS4DegAGfCBpuJnNrerA3rJ1zhWGstL0SxoWLIqrJXGpblDyg4Fhcb93gVaSNgD2A0aa2ZwYYEcCfao7tgdb51xhKC9Pv2RAUrGk8cBMQsB8L24aHLsKbpTUKKZ1BCYn7T4lplWVXiUPts65wpBBsJU0QNLYpGVA5WLMrMzMtgE6ATtK2hK4ENgc2AFoA1yQ7ep7n61zriBYBhfAzGwIMCSj8szmSXod6GNm18XkZZLuAc6L61OBDZN26xTTpgK9KqWPqu543rJ1zhUGK0+/pCGpvaRW8XUTYF/gi9gPiyQBhwAT4i7DgRPiXQk9gflmNh14CegtqbWk1kDvmFYlb9k65wpDdh7X3QAYKqmY0Nh81Myek/SapPaAgPHA6TH/CKAfMBFYApwEYGZzJF0JjIn5BpnZnOoO7MHWOVcYsvBQg5l9DGybIn2vKvIbMLCKbXcDd2d6bA+2zrnC4APROOdcHhT447oebJ1zhcFbts45lwc+eLhzzuWBt2ydcy4PvM/WOefyoKystmuwRjzYOucKg7dsnXMuD7zP1jnn8sBbts45lwfeZ+ucc3ngLdtVSfoP1Uw1YWZn5eK4zrm1l3nLNqWxOSrXOVdfect2VWY2NBflOufqMX9ct2pxMN4LgC2Axon0qsaOXBstW/YL/Qeezy/Ll1NWWsa+e+7Kmacez7tjx3H9LXdRXm40bdqYwRefy0adOnDtv2/n/Q8/BuDnZcuYM3ce77z0ONNm/MjZF15JeblRWlrK7w8/iKMO3b+Wz85VpVGjRox67QkaNmpEgwbFPPnk81wx6Hr22nNXrrnmEoqKili8aDEnn3oO33wziQ037MA9d/2blq1aUFxcxMUX/50XXnyttk+jbikt7G4EhbFxc1S49DLwCGE+n9OB/sAsM0s7mdryn77NXcXyyMxYuvRnmjZtwvLSUk444zz+dvZpXHTV9dx0zaX8qvNGPPzkc3zy2ZcMvuTcCvs+8NgzfP71N1x10V9Yvnw5ZkbDhg1ZsmQphxx/Ovf/9wbWbd+2ls4se5p02K22q5AT66zTlMWLl9CgQQPeGPUU5/zlMu65598c9ruT+OKLiZx+Wn922GEbTjn1HG679VrGj/+U24cM49e/7sazz9zHJpv2rO1TyLrSX6Zqdfdd8u/T08aEpmf/t9ryJTUG3gAaERqbj5vZZZK6AA8DbYEPgOPN7Jc4y+4wYHtgNnCUmU2KZV0InAKUAWeZWbXT4uR6DrK2ZnYXsNzMRpvZyUC9adUCSKJp0yYAlJaWUlpaiiQELF68BICFixbTvt2qQXPEK6Ppt08vAEpKSmjYsCEAvyxfTnkO/0i67Eh8viUlDWhQUoKZYWa0aN4cgJYtmzN9+o8AmEGLFs1CeosWK9JdkrKy9Et6y4C9zGxrYBugT5xb7FrgRjPbBJhLCKLE/+fG9BtjPiRtARwNdAf6ALfGqXaqlOtbvxLTYU6XtD8wjTBNcL1SVlbGkSefxQ9Tp3HMYQewVffNueJvf+aM8y6lcaOGrLNOUx4ccmOFfabN+JGp02ew0/Zbr0ib/uMs/nj+pUyeMp1zB56yVrRq12ZFRUW8/96LbPKrztz233t5f8w4TjvtPJ4dfh9Ll/7MgoUL+e2uBwIw6MrreWHEgwz848mss04T9utzdC3Xvg4qX/MGRpzmZlFcLYmLERqBv4/pQ4HLgduAg+NrgMeBm+OkkAcDD5vZMuA7SROBHYF3qjp2rlu2V0lqCZxL6Eq4EzinqszJc77fOeyhHFctf4qLi3li6C28+tR9fPLZV3z97SSGPfIUt103iFefvp9D+vXmHzfdUWGfF14ZTe9eu1JcvPKP5QbrteepYbcx4pG7eOaFV/hpztx8n4qrgfLycnrs0JuNu/Rghx7b0r37Zpx99h848KDj6dy1B0OHPsJ1/7wMgKOPOoRhwx6jc9ceHHjQCdx7702E32m3QnZatkgqljQemAmMBL4B5plZ4nngKUDH+LojMBkgbp9P6GpYkZ5in5RyGmzN7Dkzm29mE8xsTzPb3syGV5N/iJn1MLMep55wTC6rVitaNG/GjtttxZvvjOXLid+yVffNAei79+6Mn/BZhbwvvDKavvv2SlnOuu3bsknXjfnwowkpt7u6Zf78BYwa/T/67LcnW/1mC94fMw6ARx8bzs479wDgpJOO5rHHnwXg3fc+oHGjRrRrV+++BFbLysvTLskNtrgMWKUcszIz2wboRGiNbp6P+uc02EraVNKrkibE9a0kXZLLY9Y1c+bOY8HC8K3l52XLeGfMOLp23pBFi5cw6YcpALw9ZhxdN95oxT7ffj+ZBQsXsc2Wv16RNmPmLH5etgyA+QsWMu7jz+i8Uac8nomriXbt2tCyZQsAGjduzD57784XX0ykZcsWdOvWFSCmfQ3A5B+msteeuwKw+eab0LhxI2bNml07la+rMmjZJjfY4jKkquLMbB7wOrAz0EpSolu1EzA1vp4KbAgQt7ckXChbkZ5in5Ry3Wd7B3A+cDuEaYQlPQhclePj1hmzZs/l4quuo6y8HCs39ttrN3r9dicuv+Aszrl4MCoSLZo348oLV/auvPDKaPrus0eFr5HfTprMP2++A0mYGScecxib/qpLbZySy8AGG6zH3Xf9i+LiIoqKinj88Wd5fsQrnHbG+Tz6yBDKy415c+dx6oBwB8r5Fwzi9tv+ydln/wEz45RTq+xtq7+y0Gcbb0ddbmbzJDUB9iVc9HodOJxwR0J/4Jm4y/C4/k7c/pqZmaThwIOSbgA6AN2A96s9do5v/RpjZjtIGmdm28a08bEJX6215dYvl97aeuuXW9Wa3Pq1+NKj08aEdQY9nO7Wr60IF8CKCd/sHzWzQZK6EgJtG2AccJyZ/X979x4mVXXme/z7AwRUQEABjaKgkng0XkNExRiNV0y8xmucgMaIyeg45sQ5o2aMRkefTDxJRsckZ9B4S1TUY1RMjEJMHGW8BLyhgESOoggqAgoIAnbXe/7Yq6VotFV4XQAAGLVJREFUu7uqsXZ1Vffv47Oe3rVq116r7Obt1avWfteatFTsN8CewFLglIh4NV3rB8C3gAbg/Ij4Y1tt5z2yXSxpB1KeBEknAG/l3KaZdUYVuIMsImaQBc7m9a+Szd82r18NnNjKta4Eriy37byD7TnABGAnSQuA14DTcm7TzDqhqPM7yHINtum3xSGSNiUbsq8iWwj8ep7tmlknVIE5246Uy2oESf0kXSTpOkmHkgXZccBc4KQ82jSzTq5C62w7Sl4j29+Q3fL2JHAW8ANAwHER8XxObZpZZ1bnI9u8gu32EbErgKQbyD4U2zZNNpuZtVs0OMViS5pyIhARjZLedKA1s0/FycNbtLuk5elYwMbpschyQfTLqV0z66w8sv2kiGgz1ZiZWXvleQNWNXh3XTOrD/6AzMwsf/6AzMysGjyyNTPLXzQ42JqZ5a8rjWwlDQCGpsw5ZmZVU+8j25K5ESQ9mnIdDASeBa5PCXPNzKqnUEapYeUkotksIpYDxwO3RsQo4JB8u2Vmtr5oiJKllpUTbHtI2oosW9fvc+6PmVmLolC6lCJpqKS/SJolaaakf0z1l0laIOn5VI4ses1FkuZKmiPp8KL6I1LdXEkXlmq7nDnby4GHgakRMS1tH/FKGa8zM6uYjzca/3QagO9HxLOS+gLPSJqSnvt5RPzv4pMl7UyWg3sXsr3G/iTps+npX5DtYfYmME3SpIhYf5vsIiWDbUTcDdxd9PhV4OtlvzUzs0qowJxsRLxF2porIlZImg1s3cZLjgEmRsQa4DVJc1m3fc7cov3IJqZz2x9sJf0Hae+wVjp9XhsdNDOrqEIZI1tJ44HxRVUTWtvOXNIwsv3IngZGA+dKGgtMJxv9vkcWiJ8qetmbrAvO85vVj2qrb22NbKe39UIzs2oqZ042BdYWg2sxSX2Ae8h2xV0u6VfAFWQDzCuAn5LtnFsxrQbbiLilWec2iYhVlWzczKxc0bjBu6CvR9JGZIH2toj4HUBEvFP0/PWsWwywABha9PJtUh1t1LeonHW2+0qaBbycHu8u6ZelXmdmVklRUMlSiiQBvwZmR8TPiuq3KjrtOOCldDwJOEVSL0nDgRHAX4FpwAhJwyX1JPsQbVJbbZezGuHfgcObLhQRL0g6oIzXmZlVTKEyI9vRwDeBFyU17Yd4MXCqpD3IphHmAWcDRMRMSXeRffDVAJwTEY0Aks4lW6nVHbgxIma21XBZt+tGxPzsF8LHansbSzPrdMqZsy15jYipZDvGNPdgG6+5EriyhfoH23pdc+UE2/mS9gMizXX8IzC73AbMzCqhQiPbDlNOsP0OcA3ZcoeFZMPmc/LslJlZc+XMydaycm5qWAycVoW+mJm1qt6DbTmrEbaX9ICkdyUtknR/umXXzKxqCo0qWWpZOYlobgfuArYiuzf4buCOPDtlZtZchEqWWlZOsN0kIn4TEQ2p/BbonXfHzMyKNTaqZKllbeVGGJgO/5jSh00kW4N2Mu1Y7mBmVgm1PnItpa0PyJ4hC65N7/DsoucCuCivTpmZNVfrc7KltJUbYXg1O2Jm1pZ6X41Q1h1kkj4P7EzRXG1E3JpXp8zMmmsslPMRU+0qGWwlXQocSBZsHwTGAFMBB1szq5qo7S3GSirnV8UJwMHA2xFxBrA7sFmuvTIza6ax0K1kqWXlTCN8GBEFSQ2S+gGLWD+PYy76bnNg3k1YjVj+8+M6ugtWB+p9ZFtOsJ0uqT9wPdkKhQ+AJ3PtlZlZM7U+ci2lnNwIf58O/4+kh4B+ETEj326Zma2v0FnX2Uraq63nIuLZfLpkZvZJjZ012JJteNaaAL5S4b6YmbWqEneQSRpKtpJqCFkcmxAR16Q7Zu8EhpHt1HBSRLyXttG5BjgSWAWc3jTQlDQO+Jd06X9tvm9jc23d1HDQp3lTZmaV1NjiBgvt1kC2TfmzkvoCz0iaApwOPBIRP07pCS4E/plsqeuIVEYBvwJGpeB8KTCSLGg/I2lS2v68RfU942xmXUYhSpdSIuKtppFpRKwg23Vma+AYoGlkegtwbDo+Brg1Mk8B/dPmkIcDUyJiaQqwU4Aj2mrbwdbM6kIBlSySxkuaXlTGt3Y9ScOAPYGngSER8VZ66m2yaQbIAvH8ope9mepaq29VWbfrmpl1tHKmESJiAjCh1HmS+gD3AOdHxPLiDW0jIiRVfFVvOTs1SNLfSfpherytpL0r3REzs7YEKlnKkTauvQe4LSJ+l6rfSdMDpK+LUv0C1r+Ja5tU11p9q8qZRvglsC9wanq8AvhFGa8zM6uYhjJKKWl1wa+B2RHxs6KnJgHj0vE44P6i+rFp0LkPsCxNNzwMHCZpgKQBwGGprlXlTCOMioi9JD0HkJZD9CzjdWZmFVPuyLWE0cA3gRclPZ/qLgZ+DNwl6UzgdeCk9NyDZMu+5pIt/ToDICKWSroCmJbOuzwilrbVcDnB9iNJ3cmWNyBpEFAo842ZmVVEgz59sI2IqdBq1D64hfMDOKeVa90I3Fhu2+VMI1wL3AsMlnQlWXrFq8ptwMysEqKMUsvKyY1wm6RnyKK+gGMjYnbuPTMzK1KJkW1HKid5+LZkcxUPFNdFxBt5dszMrFitj1xLKWfO9g+s2/ixNzAcmAPskmO/zMzW01DfA9uyphF2LX6csoH9fSunm5nlolCZ1Qgdpt13kKUEDqPy6IyZWWvqfCfzsuZs/2fRw27AXsDC3HpkZtaCel9vWs7Itm/RcQPZHO49+XTHzKxlnXpkm25m6BsRF1SpP2ZmLeq0I1tJPSKiQdLoanbIzKwlnXlk+1ey+dnnJU0C7gZWNj1ZlC3HzCx3nXZkW6Q3sIRsz7Gm9bYBONiaWdU0dnQHPqW2gu3gtBLhJdYF2Sb1fjOHmdWZQieeRugO9KHlDDkOtmZWVZ15GuGtiLi8aj0xM2tDZ55GqPNBu5l1JvU+jdBWPttPJNI1M+sojWWUUiTdKGmRpJeK6i6TtEDS86kcWfTcRZLmSpoj6fCi+iNS3VxJF5bT/1aDbaktHszMqqlAlCxluBk4ooX6n0fEHqk8CCBpZ+AUsgyHRwC/lNQ93ez1C2AMsDNwajq3Td7K3MzqQiXmbCPiMUnDyjz9GGBiRKwBXpM0F2jaWXxuRLwKIGliOndWWxcrZ1scM7MOVyijSBovaXpRGV/m5c+VNCNNMwxIdVsD84vOeTPVtVbfJgdbM6sLDYqSJSImRMTIojKhjEv/CtgB2AN4C/hpHv33NIKZ1YW8FvdHxDtNx5KuB36fHi4Ahhaduk2qo436Vnlka2Z1oYEoWTaEpK2KHh5HdtcswCTgFEm9JA0HRpDljJkGjJA0XFJPsg/RJpVqJ9eRraTPkg3Rh0TE5yXtBhwdEf+aZ7tm1vlUYmQr6Q7gQGALSW8ClwIHStojNTEPOBsgImZKuovsg68G4JyIaEzXORd4mOxO2xsjYmaptvOeRrge+CfgPwEiYoak2wEHWzNrlw0duRaLiFNbqP51G+dfCVzZQv2DwIPtaTvvYLtJRPxV6+/33pBzm2bWCdV7Qpa8g+1iSTuQ/j9JOoHs0z4zs3ZprPNwm3ewPQeYAOwkaQHwGnBazm3WvG7duvHEE79n4cJ3OP74M7j++p/ypS+NYtmyFQCcddb3mTEjWx99wAH7cPXVl7LRRhuxZMlSDj30pI7surVhTUMjZ94znbWNBRoLwSE7DuG7++zAxBfe4Pbn32D+sg/581lfZsDGPQGICH7y2Bz+e95ievfozo8O3YX/MbgfC5d/yPf/8AKFCBoKwSm7D+XEXYeWaL3z68xZvyphQEQcImlToFtErJD0NeD1nNutaeee+y3mzJlL377r9tK86KKruPfe9aeANtusH9dccyVHH/1N5s9fyKBBm1e7q9YOPbt3Y8JxX2CTnj34qLHAt/7vNEZvtzl7bNWfA4YP4tv3TF/v/KmvL+aN91dx/9jRvPj2Mq76y2x+c/IoBm3ai1tO3JuePbqxam0DJ9z2JF8ePojBfXp30DurDfU+ss176df1kj4fEStToD0FuCTnNmva1ltvyZgxB3PTTRNLnnvyycdw//1/ZP78bOf4d99dknf37FOQxCY9s/FLQyEblUpip8H9+Ey/jT9x/n+9+i5f22krJLHbVv1ZsaaBd1euYaPu3ejZI/unubaxQNR3jKmYCuVG6DB5B9sTgFsl7STpLLJphcNybrOmXX31ZVx88VUUCuv/UfSjH/0T06Y9zE9+8kN69sz+zBwxYnv699+MyZPv5Ikn/sBpp329I7ps7dBYCE6+/UkOvuG/2Gfbzdl1y81aPXfRB2vYsu+60eqQPr1Z9MFqAN5esZqTbnuSMTc9zulfGNblR7WQjWxLlVqWa7BNiRpOIduv7OvAYRGxrLXzi+9rbmz8IM+udYgxYw7m3XcX89xzL65Xf8kl/8Zuux3E6NFHMXBgfy644LsA9OjRnT333JVjjz2do476Oy666Dx23HF4R3TdytS9m7jzG/vy8Le+xEtvL2Pukg37Od6yb2/uOm1f7h87mgdeXsiSVWsq3NP6U05uhFqWy5ytpBdZf6XGQLLFv09LIiJ2a+l16T7mCQC9e29b27+mNsB++43kq189lCOOOIhevXrRr19fbrrp3znjjPMBWLt2Lbfeehfnn382AAsWvM3Spe+zatWHrFr1IVOnPs1uu+3M3LmvdeTbsDL07bURI7cZwBOvL2bHzfu0eM7gPr14e8Xqjx+/88HqT4xgB/fpzY6b9+HZBe9z6Ighufa51kWNj1xLyWtk+zXgqKIyimz6oOlxl3TJJf/GjjuO4nOfG83Ysefy6KNPcMYZ57PlloM/Pueoow5n5sw5ADzwwGT22++LdO/enY037s0Xv7gnL7/8Skd130pYumotK9Z8BMDqhkaenr+UYQM2bfX8Lw8fxO9ffouIYMZb79OnVw8GbdqLd1asZnVDllBw+eqPeG7h+wwbsElV3kMta4goWWpZLiPbiFhvtYGkwWRbolsLbr75GrbYYnMkMWPGTM4992IA5syZy+TJjzJ9+mQKhQI33TSRWbP+1sG9tdYsXrWGH06eSSGCQgSHjhjCAcMHcfvzb3DLM/NYsmotJ93+JPtvtwWXHrIL+w/bgqnzFnP0Lf9N7426c9khWf7p195byc8m/S3bmCpg7F7bMWKLvm033gXUdigtTZHjbwNJR5OlK/sMsAjYDpgdEbuUem1nnEawli396dEd3QWrkk3OuW6DdxI7dbtjS8aEO16/r2Z3Kst7NcIVwD7A3yJiONm+Zk/l3KaZdUL1/gFZ3sH2o4hYAnST1C0i/gKMzLlNM+uEGimULLUs7zvI3pfUB3gMuE3SImBlzm2aWSdU26G0tFxGtpK2TYfHAKuA7wEPAf+PLrwawcw2XGMUSpZaltfI9j5gr4hYKemeiPg6cEtObZlZF1DbobS0vOZsiz8R3D6nNsysC6nEnG3aPXeRpJeK6gZKmiLplfR1QKqXpGslzU077+5V9Jpx6fxXJI0rp/95Bdto5djMbINERMlShpuBI5rVXQg8EhEjgEfSY4AxZPuOjQDGk23xhaSBZNvpjAL2Bi4t2v68VXkF290lLZe0AtgtHS+XtELS8pzaNLNOrBKJaCLiMWBps+pjWDfNeQtwbFH9rZF5CuifNoc8HJgSEUsj4j1gCp8M4J+Q1x1k3fO4rpl1XeWkUJQ0nmwU2mRCyrnSliER0bSDzNtAUxKKrYH5Ree9mepaq29T3ku/zMwqopzVBsXJrDZERISkXKY+876pwcysIqKM/zbQO2l6gPR1UapfABTvR7RNqmutvk0OtmZWFxojSpYNNAloWlEwDri/qH5sWpWwD7AsTTc8DBwmaUD6YOywVNcmTyOYWV2oxLY3ku4ADgS2kPQm2aqCHwN3STqTbH/Epl1VHwSOBOaS3Zx1BkBELJV0BTAtnXd5RDT/0O0THGzNrC5U4g6xiDi1lacObuHcINvKq6Xr3Ajc2J62HWzNrC7U+oaOpTjYmlldqPdtcRxszawu1HqimVIcbM2sLuS5q0w1ONiaWV2o9eTgpTjYmlldKHhka2aWP8/ZmplVgVcjmJlVgUe2ZmZV4DlbM7MqKERjR3fhU3GwNbO64Nt1zcyqwHO2ZmZV4DvIzMyqwCNbM7MqqPeRrbfFMbO60BiFkqUckuZJelHS85Kmp7qBkqZIeiV9HZDqJelaSXMlzZC014b238HWzOpCIaJkaYeDImKPiBiZHl8IPBIRI4BH0mOAMcCIVMYDv9rQ/jvYmlldiIiS5VM4BrglHd8CHFtUf2tkngL6N+3E214OtmZWF8qZRpA0XtL0ojK+hUsFMFnSM0XPD0k75wK8DQxJx1sD84te+2aqazd/QGZmdaGcaYKImABMKHHa/hGxQNJgYIqkl5tdIyRV/NM4B1szqwuVWvoVEQvS10WS7gX2Bt6RtFVEvJWmCRal0xcAQ4tevk2qazdPI5hZXajEnK2kTSX1bToGDgNeAiYB49Jp44D70/EkYGxalbAPsKxouqFdPLI1s7pQqMzIdghwryTI4t/tEfGQpGnAXZLOBF4HTkrnPwgcCcwFVgFnbGjDDrZmVhcqcVNDRLwK7N5C/RLg4BbqAzjnUzcMqN7vyuhsJI1Pk/zWyfl73bV4zrb2tLRUxTonf6+7EAdbM7MqcLA1M6sCB9va4zm8rsPf6y7EH5CZmVWBR7ZmZlXgYGtmVgUOtlUiqTElK35J0gOS+m/gdU6XdF2l+2eVUfR9birDWjlvmKSXqts760gOttXzYUpW/HlgKRW6K8VqTtP3uanM6+gOWW1wsO0YT5JyYkraQdJDKbfm45J2SvVHSXpa0nOS/iRpSJtXtJokqY+kRyQ9m7ZiOaaFc7ZP3+cvtvbzYPXPuRGqTFJ3snuwf52qJgDfiYhXJI0Cfgl8BZgK7JNya34b+F/A9zuiz9YuG0t6Ph2/BpwIHBcRyyVtATwlaVLTyZI+B0wETo+IFyQ9Qss/D1bnHGyrp+kf4dbAbLKkxX2A/YC7UxYigF7p6zbAnSm3Zk+yf7hW+z6MiD2aHkjaCLhK0gFAgez73/RXyiCyVH7HR8SsEj8PVuccbKvnw4jYQ9ImwMNkc7Y3A+8X/+Ms8h/AzyJikqQDgcuq1VGrqNPIguoXIuIjSfOA3um5ZcAbwP7ALLJpvdZ+HqzOec62yiJiFXAe2ZTAKuA1SSfCx9smN6V/24x1GeHHfeJCVi82AxalQHsQsF3Rc2uB48iSU38jIpbT+s+D1TkH2w4QEc8BM4BTyUY+Z0p6AZhJtpsnZCPZuyU9AyzuiH5aRdwGjJT0IjAWaL7f1Urga8D3JB1N6z8PVud8u66ZWRV4ZGtmVgUOtmZmVeBga2ZWBQ62ZmZV4GBrZlYFDrZdRLOsY3enmys29Fo3SzohHd8gaec2zj1Q0n4b0Ma8dHtrWfXNzvmgnW1dJumC9vbRrD0cbLuO4qxja4HvFD8paYPuJoyIb0fErDZOOZDsFlSzLs3Btmt6HNgxjTofT4lRZknqLulqSdMkzZB0Nnx8J9N1kuZI+hMwuOlCkh6VNDIdH5GyW72QMl0NIwvq30uj6i9JGiTpntTGNEmj02s3lzRZ0kxJNwCiBEn3pexYMyWNb/bcz1P9I5IGpbqSGbUknSdpVnr/Ezfsf69ZCyLCpQsU4IP0tQdZ8pPvko06VwLD03PjgX9Jx72A6cBw4HhgCtAd+AzwPnBCOu9RYCTZ/f/zi641MH29DLigqB+3A/un422B2en4WuCH6firQABbtPA+5jXVF7WxMfASsHl6HMBp6fiHwHXp+BFgRDoeBfy5eR+BhUCvdNy/o79vLp2nOBFN11Gc+u9xshSP+wF/jYimjGKHAbs1zceS3dc/AjgAuCMiGoGFkv7cwvX3AR5rulZELG2lH4cAOxdlteqXsl0dQBbUiYg/SHqvjPd0nqTj0vHQ1NclZNm17kz1vwV+146MWjOA2yTdB9xXRh/MyuJg23Wsl/oPIAWdlcVVwD9ExMPNzjuygv3oRpand3ULfSlbyoR2CLBvRKyS9Cjrsmk1F5SfUeurZIH/KOAHknaNiIZ2dc6sBZ6ztWIPA99NOViR9FlJmwKPASenOd2tgINaeO1TwAGShqfXDkz1K4C+RedNBv6h6YGkpuD3GPCNVDcGGFCir5sB76VAuxPZyLpJN6BpdP4NYGqUkVFLUjdgaET8Bfjn1EafEv0wK4uDrRW7gSyv6rPKNiP8T7K/fu4FXknP3Uq2rc96IuJdsjnf36WMVU1/xj8AHNf0ARlZesmR6QOoWaxbFfEjsmA9k2w64Y0SfX0I6CFpNvBjsmDfZCWwd3oPXwEuT/WlMmp1B36bMnQ9B1wbEe+X6IdZWZz1y8ysCjyyNTOrAgdbM7MqcLA1M6sCB1szsypwsDUzqwIHWzOzKnCwNTOrgv8P3G6h5yt4VfcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reporte de precision y recall:**\n",
        "\n",
        "Recordar que el vector `y` viene de la columna del dataset `is_fake`, que es True (1) cuando la noticia es falsa y es False (0) cuando la noticia es clasificada como real."
      ],
      "metadata": {
        "id": "R8yHyNswroS0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Classification Report (only text):\\n\\n',classification_report(ytest['is_fake'], ypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIGoNg3MI5cY",
        "outputId": "5a5e86c5-d075-4391-db2a-e0783398ecaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report (only text):\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.90      0.91      0.91      4261\n",
            "        True       0.90      0.88      0.89      3559\n",
            "\n",
            "    accuracy                           0.90      7820\n",
            "   macro avg       0.90      0.90      0.90      7820\n",
            "weighted avg       0.90      0.90      0.90      7820\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Classification Report (text + title):\\n\\n',classification_report(y2test['is_fake'], y2pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdNt8H67bxVR",
        "outputId": "4b5758f7-d69b-4eae-fbdb-8cec67727a63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report (text + title):\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.89      0.91      0.90      4261\n",
            "        True       0.89      0.87      0.88      3559\n",
            "\n",
            "    accuracy                           0.89      7820\n",
            "   macro avg       0.89      0.89      0.89      7820\n",
            "weighted avg       0.89      0.89      0.89      7820\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Noticias mal clasificadas por temática:**\n",
        "\n",
        "A continuación se muestra un gráfico de torta que muestra las noticias mal clasificadas (solo considerando su texto), separadas por temáticas:"
      ],
      "metadata": {
        "id": "viL3H63MSTiH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = ytest.copy()\n",
        "results['pred'] = ypred\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_WV8xghDFrX5",
        "outputId": "186e0230-ddd0-4e8b-ea68-34e36781bce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>is_fake</th>\n",
              "      <th>subject</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9475</th>\n",
              "      <td>True</td>\n",
              "      <td>US_News</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18140</th>\n",
              "      <td>False</td>\n",
              "      <td>politicsNews</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6115</th>\n",
              "      <td>True</td>\n",
              "      <td>News</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13852</th>\n",
              "      <td>True</td>\n",
              "      <td>left-news</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18781</th>\n",
              "      <td>True</td>\n",
              "      <td>Middle-east</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          document.querySelector('#df-e0bf0faf-1fc0-4cd2-9777-86ec0c4fe661 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-e0bf0faf-1fc0-4cd2-9777-86ec0c4fe661');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       is_fake       subject   pred\n",
              "9475      True       US_News   True\n",
              "18140    False  politicsNews  False\n",
              "6115      True          News   True\n",
              "13852     True     left-news  False\n",
              "18781     True   Middle-east  False"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_errors_dtree = results[results['is_fake'] != results['pred']]"
      ],
      "metadata": {
        "id": "0Sc78U35GDRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtree_errors = df_errors_dtree.groupby('subject').size()\n",
        "dtree_errors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaZed1fSGhTk",
        "outputId": "095a14da-2208-473c-d388-926c1f7ae5e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "subject\n",
              "Government News     51\n",
              "Middle-east         22\n",
              "News               113\n",
              "US_News             25\n",
              "left-news           80\n",
              "politicsNews       364\n",
              "worldnews          149\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(dtree_errors, labels = dtree_errors.index.values.tolist(), autopct='%1.0f%%', colors=colorsitos, shadow=True)\n",
        "plt.title('Noticias mal clasificadas por el Decision Tree por temática')\n",
        "plt.savefig(\"pie-DT-mal-clasificadas.pdf\", bbox_inches='tight')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "ilv6rUuIHljM",
        "outputId": "efeddb94-2ffc-4bfa-e17a-cf3942ff11ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Y ahora se realiza el mismo procedimiento pero considerando los textos sumados a los títulos de las noticias:"
      ],
      "metadata": {
        "id": "YB5MrmgtSmD3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results2 = y2test.copy()\n",
        "results2['pred'] = y2pred\n",
        "results2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WJ-ShfAyM6CF",
        "outputId": "26859dcd-94be-412c-b42d-79c3d361ea3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dfe3d3fa-e4d5-41f0-8bd1-2475ef067cab\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>is_fake</th>\n",
              "      <th>subject</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9475</th>\n",
              "      <td>True</td>\n",
              "      <td>US_News</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18140</th>\n",
              "      <td>False</td>\n",
              "      <td>politicsNews</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6115</th>\n",
              "      <td>True</td>\n",
              "      <td>News</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13852</th>\n",
              "      <td>True</td>\n",
              "      <td>left-news</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18781</th>\n",
              "      <td>True</td>\n",
              "      <td>Middle-east</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfe3d3fa-e4d5-41f0-8bd1-2475ef067cab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dfe3d3fa-e4d5-41f0-8bd1-2475ef067cab button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dfe3d3fa-e4d5-41f0-8bd1-2475ef067cab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       is_fake       subject   pred\n",
              "9475      True       US_News   True\n",
              "18140    False  politicsNews  False\n",
              "6115      True          News   True\n",
              "13852     True     left-news   True\n",
              "18781     True   Middle-east  False"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_errors_dtree2 = results2[results2['is_fake'] != results2['pred']]"
      ],
      "metadata": {
        "id": "HHRSicV4Nj3_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtree2_errors = df_errors_dtree2.groupby('subject').size()\n",
        "dtree2_errors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SL-ICxO2Npka",
        "outputId": "dc6791b1-73ba-4d1a-a13f-f3019b9ff697"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "subject\n",
              "Government News     51\n",
              "Middle-east         28\n",
              "News               107\n",
              "US_News             23\n",
              "left-news           87\n",
              "politicsNews       391\n",
              "worldnews          157\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(dtree2_errors, labels = dtree2_errors.index.values.tolist(), autopct='%1.0f%%', colors=colorsitos, shadow=True)\n",
        "plt.title('Noticias mal clasificadas por el Decision Tree por temática (title + text)')\n",
        "plt.savefig(\"pie-DT-mal-clasificadas-con-titulo.pdf\", bbox_inches='tight')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "7SuTFzB0Nuci",
        "outputId": "5b6edd75-5267-41e1-ebf2-cfc3d459c2a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w1GAwGAz+UdWbwi1DW0KtDTcC6oI1m0cbDAaDoVNgujQNBoPB0CkwFp7BYDAYOgVG4RkMBoOhU2AUnsFgMBg6BUbhGQwGg6FTYBSewWAwGDoFRuEZDAaDoVNgFJ7BYDAYOgVG4RkMBoOhU2AUnsFgMBg6BUbhGQwGg6FTYBSewWAwGDoFRuEZDG0cEekjIr/VCbtNRK4TkZEi8oOILBCRJSJyWwP5nCsiXtt5bG3YbyLSp9WENxjaEMY9kMHQvpkKnKaqv4qIk8ad264DbgZOb3XJDIY2hrHwDIb2TRawEUBVPaq6uJH0HwN7iMguilFEjhSReSIyX0TeFpEEERkhIu/Z8SeISKWIRIlIjIistMOvFJHFIrJQRN4I8vUZDEHDKDyDoX3zMLBMRN4XkUkiEtNIei/wALCTE1ERyQBuAcaq6jAgD5gM/AIMtZONBn4DRgAHAD/Y4TdieZveG7ik5ZdkMLQORuEZgoqIlAeQ5kp7vOlVETlRRIaEQrZ2TH1OK1VV7wCGA9OBM4HPA8jvNWCkiOzmEzYSGAJ8KyILgIlAb1V1AytEZHdgfyAXOARL+c21z10IvCoiZwPuJl2ZwRBCjMIzhIPLgCNU9SzgRKyG1lA/W4HUOmFpQAGAqq5Q1aeBw4F9RCS9ocxsJfZv4AafYAG+VNWh9t8QVb3AjpsDHAPUAF8BB9t/tQrvOOBJYBjwk4iYuQGGNolReIZWQ0SmiMhP9tjO7XbYM0Bf4DMRuRkYBzxozzLsV+f8PrYl+JyI/C4i00Uk1o7rJyKfi8jPIjJXRAaLiFNE/hKLFBHxiMghdvo5IjJARA61y1ogIr+ISGJoa6XpqGo5sFFEcgBEJA04GvhGRI4TEbGTDgA8QHEA2b4IjAUy7d/fA6NEpL9dRryIDLTj5gJXA/NUNR9Ix5oc85uIOICeqjoTS4EmAwktuV6DobUwCs/QKojIkVgN8P5YY0D7icghqnoJsAE4TFXvBqYBU2yrYoWfrAYAT6rqHlgN+Sl2+H+AK1R1P+A64ClV9QDLsCzGg4H5wGgRicZqlP+w016uqkOxuuUqW+P6W4FzgP+zuxtnALfb9TUBawxvAfAycJZdDw2iqi7gMaxJL9iK7FzgdRFZCMwDBtvJfwCysSw9sLowF6mqAk7gFRFZhDXe95iqBqJwDYaQI9YzazAEBxEpV9UEEXkIGM8OayMBuFdVnxeRVcBwVS0QkReBj1X1HT959cHqZhtg/74BiAQeAfKxlFst0aq6u201FgK7YVktFwF3A1eq6mkiciNwEvAq8J6qrgtqBRgMhjaLsfAMrYVgKbjaMaH+qvp8gyeI9PTpbqyd7Vftk8SDtXbUART75D1UVXe308zBstz2Bz4FUoAx2ONNqnofcCEQizVBYzAGg6FTYBSeobX4AjhfRBIARKS7iGT5SVcGJAKo6lofBfZMfRmrainwl4icauctIrKPHf0jcBDgVdUqYAEwCbs7TkT6qeoiVb0f+Ikd3XYdBhE5z+fDofbvyXDLZTCEG6PwDK2Cqk7Hmv4+zx7feQdbsdXhDWCKPYGkn5/4+jgLuEBEfgV+B06wy60G1mJ1Z4Jl2SUCi+zfV9vbaS3EmnX4WdOurO2jqi/UsX6Hqurl4ZbLYAg3ZgzPYAgCry87NQ4YiDXJphfWMgJ/f3FY3b3ElaUuPGDmOYOwum2rgG1YSw222H8bsJT3WmB1lykHm8kgBkMLMOtlDIYm8PqyU2OwdhoZjjU1fyAwUFW7+SwPCIiK0pptWEs0AmLTg9/kY1mqvy2Jdc77JTFyCfD75InDzWJvgyEAjIVnMDTA68tOzQRGAaNU9WBgmIhEBSNv1/L4zUctvTC7Oef+kBjx44rYiP2xllUswBq7/BKYMXni8Pay1MJgCClG4RkMPry+7NQIrFme41T1GH+bLAcLz5+Ja8YuPr9Xc859O0FLa+Jik/xEVQIzgU+AjydPHL6mJTI2FRFRIFdVr7V/XwckqOptoZTDYPCH6dI0dHpeX3ZqFHCkevU0hXEOhyQDNLGHsulo8/KvctdU18Ql+lN2YC23ONb+ezJ3at5vWMrvE+DbyROHe5tVaOBUAyeLyL2qWtDKZRkMTcLM0jR0Wl5fdur+ry4e/4LXqwXAR+KQCbXKLhSINu/12+quLGpC8j2xtvyaA/yVOzXv/3Kn5nVtVsGB4cbaBeeauhEikiki79rbzf0kIqPs8EX2VnAiIltF5Bw7/CUROUJE9hCRH+3lFQtFZEArym/owBgLz9CpeH3ZqXFer57lcXuvioxy7uFwtrIV1wDilWaNJ+Sru6qZRfYC7gD+lTs1bxrwDPDV5InDgz2u8SSwUEQeqBP+KPCwqn4jIr2w1mruDnyLNU66GliJ1aX8EnAgcClwH/Coqr5qj586gyyvoZNgFJ6hU/D6slMH17g8VzqcMsHpdCQ4osLfZjbbwotytlT4COBk++/P3Kl5/wFemDxxeFC6IFW1VEReAq5k571KxwJDfLqKk+yNCeZiuRxaDTwNXCwi3YEiVd0mIvOAm0WkB9Z2cH8EQ05D58N0aRo6NK8uPmWfFxecNB1YEhnlvNTpdLSdnfybaeGVxsfGB1GK/lgOYdflTs17OndqXrcg5fsIcAHgK6sDGOmzGL677Qmidju40cAsrH1Sx7NjO7jXsLxqVAKf1nqNMBiailF4hg7JS4tO3vP5vBO+EocsiI6NOCLc8vhD1NHk/lS31+OpiI9PaQVxorG8lf+ZOzXvgdypeWktyUxVC4G3sJReLdOBK2p/iMhQO+1aIAMYoKorgW+wvFrUbgfXF1ipqo8BHwJ7t0Q2Q+fFKDxDh+KlhScP/u9PJ3waEelYGJcYdXirz7RsAaKOJlt4xTWVReJ0tuZ7GwtMAVbmTs27JXdqXkss4n9jKbJargSG2xNPFmMp2Fp+AJbbx3OB7liKD+A0LN97C7Am4bzUApkMnRizDs/QIXjx15NSXVWeZxKSok4VRxvWcj44f+226pDVp/ZpyjlLKgvX/NK7W7PW7jWTLcA9wDOTJw6vbiyxwdCWMRaeod3z8PRjpojI2sSU6NPai7KD5k1aKYhoulXYQrKwxuOW5U7NOy7EZRsMQcXM0jS0Wx769Kj9ElOjX+nSO6FduvgRb9MVXnFsdEwriBIIvYGPc6fmvQFcNXni8C1hksNgaDbGwjO0Ox758pjYx2YcO7Vrn8Qfk9Ki26WyA3A00cJTVbYltMqElaZwBrAkd2reOWGWw2BoMkbhGdoVt7+Rc2RiatTqzO7x5zic0q6fX9Gmdb+W1VSVeKOioltLniaQBkzNnZr3Qe7UPH9OfQ2GNkm7bjAMnYf9crpF3PvBEc/33zv1s7jEqMxwyxMMxNu09eP5nuqyVhKluZwA/JY7Ne+kcAtiMASCUXiGNs+TD/Yf9Pa/0n7fJ6XyeIfT0WGe2aZOWilwqKeVRGkJmcB7uVPzHsmdmhf+7WsMhgboMI2HoeMxLidZ3n58t0vPPCx2Qd9uEQPHZpalRGyr6DBevx1NXHheGBMZ2VqyBIGrgM9yp+alhlsQg6E+jMIztEnG5SRHX3dB5vMnj016IjXJGQMQFSlRh3jXufF426Kl02SauvC8PCG+PpdAbYUjgB9zp+YNCbcgBoM/jMIztDlOPTol66ZLsr46ZETCec46E1OykyRjUOGadeGSLZg0xcKrdLsqa2Jj284+oPXTH/g+d2reuHALYjDUxSg8Q5viotPSB9z6j+w5I4fGH1xfmn0zXb2TCws2hFKu1sChgfsmKqipbE9duYnAB7lT824OtyAGgy9G4RnaDDdNyh550yVZs/YcGDuooXQiwmEJhanOqqryUMnWGjRl4XmBeFytKEprIMBduVPzXsudmmc2uDC0CYzCM4SdcTnJcs/kriddNTHj0916RAfkniY2WmIPrFpbruptt5vBNqVLszAqor2+q38H3sydmteWJ9wYOgnt9SUydBDG5SQ7xhyQcOnlZ2W8nJ0R2aQZfj1T6bJb/rrVrSVbayMa+Cz+kuD6wAs1J2OUnqENYBSeIWyMy0mO3GdwzDUXnpr2YHKis1kN+gGZVb3ji4s3B1u2UBDoGJ7b63FXxce19+n+JwFvGaVnCCdG4RnCwric5Ih+vaImX3Ne5q1JCc645ubjcIgcFrMlTlyuymDKFwocAW4tVuiqLMIR+ASXNsyJGKVnCCNG4RlCzricZEfPrpGT/nlJ9o1pyRGJLc0vMVYSR5SvKQyGbKEkUAsvX9ufMm+AE4G3jdIzhAOj8AwhZVxOsmSlR5x90yXZt2WlRQRt5/9+6dq9W/76djWeF6jC2xp6H3itzQlYSs9sRWYIKUbhGULGuJxkSU1ynnzLZdkPdM+OzAh2/qPSt/WIKSvbGux8WwsJUOEVx0XHtrYsYeAE4IFwC2HoXBiFZwgZDgdH3DgpK7dP96js1sg/winOMc6NDnG7a1oj/2ATiD88r6puS0wItw+81mJy7tS8ieEWwtB5ENWO1ltiaIuMy0kefemZ6Y8dc0jS0NYua3G+Y82vGf16tXY5zcVV7eGOs2dBQaw7whsVcfxeY5hy5AVc9todLN20grG7H8RNx0wC4K7Pnq3atuc+MXvtNyasMrci1cBhkycOnxduQQwdH2PhGVqdcTnJAw4/MOGWo0Yn7hOK8oZkentlFGxeG4qymkNklINbXjyUN/55V/FXV7/AzGU/MG/lAmIjo5gxeSq/rltKaWU5m0sL+HHN73RgZQcQjeVeqEe4BTF0fIzCM7Qq43KSU/v2jLrp4jPSD3FI0zx8t4RDU0qym+tK6Nmb8rjkoI+4/m/Tt4eVF7u45/w5XHPU59xz/hzKS6ydvn78Yh1Tjp/O7WfNpKyoGoDNa8p57Jrv681fRIiJj8ChTqnxuKnxuFFVKmtceL1eajxunA4HD0x/nlPGntnuZp82gy5Ye292xLFKQxvCKDxDqzEuJzkyPtbxjxsnZR0fG+2ICWXZLXEldMhJvbnhuZ33rp723FL2HJnFw18czZ4js/jouaUAfPHKn9z1dg6Hn96X7z62jMq3HvmdU6/ao8EyvB7ljPtuTtnrjnEcOnAEB/Xbl/SEFI589AKO3H0Uf21dj6qSNWCvporfXtkPeCHcQhg6NkbhGVqFcTnJAoyfcmHmOV0ygj8jMxCa60po9xGZJCRH7RT289cbGH1ibwBGn9ibvK8sZw3iEGpcXqorPTgjhaV5+aRkRtO1T8PLCx1O4Z0bHiibf/O7/LJmCUs3reTOcVfy1TUvcMmhZ/DAF//l+iMv5O3Zb2W89OQ/+X7W+029jPbI6blT8yaHWwhDx8UoPENrMeKM41L+MWyPuP7hFCJYroRKtlaTmmX1uKVkxlCy1eq+POHiwdxz3hzmz9zIQcf14v2nlnDSpYH5P3V4nZIcm8iofvsyc9kP28M//30ue3cfRH5FcWVBcX7UOZffy695M3BVV7X0MtoD9+ROzdsz3EIYOiZG4RmCzric5J49u0ZePf7olOHhlqU1XAmJiOX8BthrVDb3vDeWKc+MIu/rDQw9tCsbV5XxyJXzeO7/fqa60r3L+aWF1WwrdeHA4aisqWb2H3n0z7QmldZ43Dw3920uG3MmGyqKy8UuSL0ePO1jtUVLiQZezp2aF9VoSoOhiRiFZwgq43KSo4BLrzk388CoSGkTjVZstMQe1EJXQsnp0RRtsXb4KtpSSXJa9E7x1ZVu5ry/iiPO7Mc7jy/mkvtGMGhYOt9+tGaXvIrzK7lr4hxOfODa+GMeu4hDBwzniCGjAHjhu/c4bfjRxEXFkNFttwqXq4oHbzmDHn12Jza+xbuwtReGOtXdLOexIqIi8orP7wgRyReRj+3f40TkxnrO9ftRJCIvish4+3iWiIT8Q05EzhWRgFxnGerHOGY0BJujTj4y+aD+vaP7hFsQX3qk0mW3zetWrcrq1ac55w/L6cbcD1Yz7uLBzP1gNfsdvnPb8/HzyzlqQn8iIh3UVHsQscb3qqt2nTPTa1AK974/lv0+mVCV5EnbaePsi0eftv24KDrSOeHSuxuV7Y3n72DJgm9ISEplyt1vAvDZu0/z+y9zEBESktI448JbSU7NZOFPM/j8/WeIS0jmvCsfJBeYVHEAACAASURBVD4hhYIt6/j0nSc557J7m1M1QSfFXTjv1NIPL/HkPvehc/Lz85t4+jZgTxGJVdVK4AhgfW2kqk4DpgVR3FBxLvAb0OLu+c6MsfAMQWNcTnKPzDTnGacfmzIi3LL4I1BXQo9P/oFb/z6TjX+V8Y9DP2HmO38x7qJBLPpuM9cc9TmL5m1h3EWDt6cv2lzJikWFjBjbHYAjz+7PLafO4Os3VjLq+PrXvze2l2ZJfGxCINc14uDjuejax3YKO+zYCVx31+tce+drDBl6MF9++F8AvvnqTa6+9SUOHHMS8+d9AVjK8ZiTLw2kqFbF7aqq7LdmWvnEkjcPjNOqLOC/ntwLmvNR/ilwnH38d+D12gjbUnrCPt5NROaJyCIRucsnjYjIEyKyTES+ArL8FSIiR9rnzxeRt0XE7/0SkSki8pOILBSR233CPxCRn0XkdxG52A5z2hblb7Zc19jW5XDgVRFZICJm+UYzMRaeISiMy0mOAM6/+tzM4bExjma7+2lNal0JfeKKq9SoqHobjStyD/AbfvOLh/oNT82O5fpndyxjGHl0D0Ye3fg66oYUXo3HXVMVH58cyMLFfoOGUZi/84d/jI+udFVXgr0EUhwO3G4XLlcVTmcEK5f9QlJyOpldwrsxjXfL8oIJntmpWfFu3w2l9wWuBe5vYnZvAP+yuzH3Bv4HjPaT7lHgaVV9SUQu9wk/CRgEDAGygcV2HtsRkQzgFmCsqm4TkRuAycAdddIdCQwA9sca+Z0mIoeo6hzgfFUttBXYTyLyLtAH6K6qe9rnp6hqsYj8A7hOVfOaWBcGH4yFZwgWY44YlXDIXgNjB4ZbkIZoS66EBEe9719hTWWROBwtWqj/6TtPccfk45g/73OOPsnaqiznuHN59oHLWfzLXPYdeRRfTnueI8Zd0JJiWoTbVVXZf8208mucX2dkRbn9eU+41ZN7Qb+m5KmqC7EUx9+xrL36GMUO6+9ln/BDgNdV1aOqG4AZfs4diaUQvxWRBcBEoLefdEfaf78A84HBWAoQ4EoR+RX4Huhph68E+orI4yJyNFDa8NUamoKx8AwtZlxOcnZUpJw58aS0/cItSyD0S9fu67asX70hs7u/BipkONVZr8LL15oW+8A7dvxlHDv+Mr7++AW++fotjj5pEoP2PIBBe1oWbN63n7D73geRv2kNsz5/hdi4RE486zqiokOzR0A9Vl1dYoH7gFObmP004CFgDJDeQLrmTmQS4EtV/ftOgSIHAM/aP/9lp7tXVZ+tk24MMBY4UFUrRGQWEKOqRSKyD3AUcAlwGnB+M2U01MFYeIYWYS8wP+fsE1IHJCU4282u/m3BlZCjAQtva2TwXs1hBx7DorydjRRXdRU/ffMxow4/jc8/+A9nXHQbuw0cyvx5nwWt3PoIwKqry3hP7gUjm1jM/4DbVXVRA2m+Bc6wj8/yCZ8DnG6Pp3UFDvNz7vfAKBHpDyAi8SIyUFV/UNWh9t804Avg/NrxPRHpLiJZQDJQZCu7wVgWY21XqUNV38XqMh1ml1cGdJppuq2FUXiGlrJHXKxj7yMPTmx1LwjBpC24EnI0YOEVx8W0aBw0f9OO5RC/zZ9NVtc+O8XP/OxlDh57Os6ICNyuagRBxIHL1bqL2735y7dOKHw56rj49QFNyPHhwaYkVtV1qvpYI8muAi4XkUVAd5/w94E/sMbuXgJ28eSgqvlYMydfF5GFdprBftJNB14D5tnlvIOluD4HIkRkCZYFW7v5andglt1N+grwTzv8ReAZM2mlZRj3QIZmMy4n2QncdsWEjJwjRiUeFG55msPifMfaXzP69Qx1uepVzfn4ar9jdF716pvpUR6NiAhoyOHlp29mxdKf2VZeTGJSOkedeDFLFn5L/qbViDhITe/C+HP/SXKqNdmwpCift1+4mwsnPwLArz9+xRcf/IfYuETOu/IhEpJSg3WZ23G7qioHb5zuPi5hfUuslBOdk5//MGhCGTodRuEZms24nORhqUnO6/5zV4+ToqNCuzl0MPkyP2ltQUZ2SJWe16Pewz+52q+FV1RdUfRZz7Tga50w4c1fvvUsz+yUrMiAui8bYiEw1Dn5edNoGZqF6dI0NAt7GcIZF52W1q89KztomSuhZqN464vK91YHbRu0cOJ2VVX2Xz2t7BrH1+lBUHZgLTE4OQj5GDopRuEZmsv+XTIieh0wNH7fcAvSUlriSqi5aAMKb6uz/rj2wvaxupZ1YfrjVk/uBSHzq2joWBiFZ2gy43KSo4HTzz0lbbfICIkMtzzBIDtJMgY3w5VQs/HWr9SKYqLaxB6kzaEVrLq67AWc0gr5GjoBRuEZmsOomGhJGzYktkN5J903qyYoroQCoSELrywhPjkUMgSbVrTq6nJ1K+dv6KAYhWdoEvbMzL+NPyolLSba0eGmRwfblVB9qNf/gudtNdXbPDEtW5IQakJg1dVllCf3gn1CUI6hg2EUnqGp7A6kjDkgvkM2OMFwJRQQKn7zL3BXlbRquUEmhFZdXS5vPInBsDNG4RmaypHD94yNy0qP7N540vZJj1S69M1ft6sju2BST5dmgXhcrVpukAiDVbcTCmd8N/Xmdtn1awgfZi9NQ8CMy0nOBvY6+cjkAY0mbufsn1nVa0tx0eZtKanZrZF/fV2ahdGBLTYPJ978P7ZO8MxKyUpwh7xLu8zlrvw1Me2PLwbtmb41Ovasg+CpUMtgaL+0+ZfL0KYYlZrkjBjcN6ZDTVbxh+VKKD/uE1d8g66Emo2KXwuvJCGuqVtuhQy3q6py8Kbp7uPi16eHsm/Iq6orXbrxy4zuEYsGDMrC6dzbjjofo/AMTcAoPENA2EsRxp52bEpWRIR0iucmMVYSR2xds/7HtP7B7771Y+G5PG5XdWxcSltcZLbdqosPnVVXXuMpnxcZX/h1/8FZpWnp3fwk2W/S3Nf2fnb0mQtDJZOhfdMpGi5DUNgbiN1397bt7y7YtJorIT+TVrbWVBaLI8Gvd+1wEWqrzrbmNkzP7BH5W/+BmTidjVm85wHXtL5kho6AUXiGQMnJSHW6umRGhNWHXDgYlb6tx7Sy0oLqxKSMoGXqx8IrCIIPvGBR46rm8dvPdid5SmNFvfy0dx9uO3oYE16ZxW+bijh2SE/uPnY4AHd/uYA9u6Rywl7NfzTKazxl30XGF83ov3tWaVpaUyzqEzEKzxAgRuEZGmVcTnIiMOi4MUmpDoe0xR63ViXCKc7DnJucX7jjajQiIjg7y/ix8AoiW+bhPFhYng2+cP9++ejEhOhIajxeDnniY3IGdCM2MoJfrjuJo575nJJKFxU1bn5ck8/NRzTdO5RXVVe4dMOXO6y55ixt6DNp7mtDnh195uJmnGvoZBiFZwiEQQD7Dondxd9XZyE1XlL3zl8dPFdC3l0VXkl8+Bec7zwD09LtNR4vbo+iKJU1brxepcbrxekQbvt8Prce1bTtVMtqPGXfRSYUzRgwOKsstUnWXH0ci+W7zmBoEKPwDIEwMi5GXL26RvULtyDhZEimt+f6/E1rCjK69GpxZrpzl6ZHvd6KhISwuQTyN1bn8XrZ/+Fp/FlQyqWjdufQfl35cNFqRjz8IWft148/C0rxqjKsR+M9vV5V/dOlG6Zn9Yxc3G9AVjOtufo4DngoiPkZOihG4RkaxJ6duc9Ro5MSIzrIRtEt4dDU0i4fbksqdsfHpbQoI+/OyxJKXJXFOOPSWpRnc0WpZwam0+Hg52tPpLiymlNe+JrfNhaRe+LI7fEnPP8lT48fxT1fLWDhhkLGDuzOhSMH7ZR3WY2n9NvIhOKZA4ZklaWmtNZmBQdPmvta8rOjz2xXu9QYQo/ZacXQGP0B5/57x3Wq2Zn1ERURJFdCuvNwXb7Xta1F+TUDt6uqsv+aaWXXOL5qcLeUlNhoxvTvyhdLdziTmPbbaob1SKfcVcPKgjLeOCeHd39dRYXLjVfVu6xa1z+R3D3/hgMOT5o24qBeZakprekzMQI4shXzN3QQjMIzNMYwoKZP987dnelLUFwJ1Zm0EmofeN78P6w9MOP974GZX15JcWU1AJU1br5avoFB2dZOXjUeL4/O+Z0ph+1NZY2b2mlMlW6P52N35NobBw53PXrw2O6LBw7OxBmyXceOCzShiGSLyGsislJEfhaReSJyUmsKFw5E5GoR8TsuLCKzRCTP5/dwEZkVMuHChOnSNNTLuJxkBzCyf6+omvg4R1K45WlL7JtV03vj1oL1JWkZzeqmkzqTVopio0PiA8/tqqoctHG6+/iEhtfVbSyt5PzX5+BRxavK+H124/gh1tDlU98u4ZzhA4iLimDPLqnetdtc1d0fmhbV5dADnLMOzQnOpJ6mc0wgiUREgA+Aqap6ph3WGxjXWoKJSISqulsr/wa4GngFqKgnPktEjlHVz0IoU1gxCs/QEF2AmAP2iTOb9PrhsITCtI+qEso9MTFN3w6sjoVXlhDfsjHBAGjKHph7d0sj79oT/cZddcgelLrcpZ9KXPHMQbtn9X36iNi+QZe2yWRNmvtav2dHn7mikXQ5gEtVn6kNUNXVwOMAIhIDPA0MB9zAZFWdKSLfAxeo6u92ulnAdcAS+9w9saa13qaqH4rIucDJQALgFJEXsJRqHNAPeF9Vr7fzKrfLPBbYCNwEPAD0Aq5W1Wki4gTuA8YA0cCTqvqsiIwBbgMKbBl+Bs4GrgC6ATNFpEBVD/NTFw8CNwM7KbwGynoS+MKW532gSFXPF5Hz7Wu6B3gL6AE4gTtV9c1G7kdIMQrP0BA9ARm4W0y4vtrbNLHREntQ0dpNc6L7xYs0cQ2dd0fy8pqqMm90Squ51wnUqmsMr6p3uYsN07N7Ri/pNyBTHG3O6h8BNKbw9gDmNxB/OaCqupeIDAami8hA4E3gNOBWEekKdFXVPBG5B5hhN/wpwI8i8pWd1zBgb1UttBXgUGBfoBpYJiKPq+paIN7OY4qtSO4CjgCGAFOBacAFQImqjhCRaOBbEZlul7OvfV0bgG+BUar6mIhMBg5T1YJ6rnUecJKIHAaU+YTXV9ZcYLQtT3egq51+NPAGcDSwQVWPAxCRNvehbMbwDA0xBKjunt1xXQG1lOa6EhJ1bLfwCtxVpcGVage1Y3XHt8BfXanLXfKpxK25YeDwmscOHttj6YBBmeJok03H/k09QUSeFJFfReQnO+hgrG5AVHUpsBoYiGW5jLfTnAa8Yx8fCdwoIguAWUAMlmUG8KWqFvoU97WqlqhqFda6wdqtaVzA5/bxImC2qtbYx318yjnHLucHIB2o9Vryo6quU1UvsMDnnEC4C7ilTlh9Zc0FRovIEFv+zbbyPxD4zpb3CBG5X0RGq2qbmzVrLDxDQ+wRGUFZWoqzVVzkdBSa40pIfGZp5os36OM7LbXqPF71LnezYXp2r5ilfftniMPR5r7W/TAigDS/A6fU/lDVy0UkA8ir/xRQ1fUislVE9gZOBy6xowQ4RVWX+aYXkQOAujNvq32OPexof2tUtfYDyFubTlW9Its3ahfgClX9ok45YxrIt1FUdYaI3AWM9An2W5ZdXgqWJTcHSMNS/uWqWgaUicgwrK7Zu0Tka1W9I1BZQkGb/EwzhJ9xOckJQNqwPeISI5ydwztCc7FdCcWLyxX4Xpg+k1YKYyKDOpWxJVZdictd8onErblx9xE1j48a22NZ/4EZbdSa80cgbqtmADEicqlPmO9MxrnAWQB2V2YvoFaZvQlcDySraq2Hhi+AK+zJMIhI07adCZwvgEtFrLWwIjJQROIbOacMCOQZuAvrugIp63usyTBzsOrqOvs/ItINqFDVV7DGB4cFcmGhxDRkhvroCniH9Ivp2mhKA4mxktAUV0KiO5RIWUJ8UMbCmmvVebzqXeZmw5fty5rzR/Kkua/1enb0mfV2MauqisiJwMMicj2Qj2WJ3WAneQp4WkQWYU1aOVdVay2od4BHgTt9srwTeARYKCIO4C/g+GBelM1/sboq59vKNR9r4+yG+A/wuYhsqGfSCgCq+qmI5AdY1lzgSFX9U0RWY1l5c+24vYAHRcQL1AC+HxVtAtlhSRsMOxiXk3woMPGmS7IGjBwaf3C45WkvzN4SF5ArIV2aujpn+Tm9qz011e92TYxuabne/D+2nuWZldLQAvK6lLjcJXNjkktmDdg9uyIpOSAZqsu2MeeB5yj8ax2CcOiNF7Nqbh5rf/iV9AG9Oexmq437Y/o3VBWXsddpAa0WCCbHPzv6zE9CXaihfWAsPEN99AeqUpOd7fVrPywE6krI67a6i7bWVBZDYrPHSJtq1Xm86lnmZuP07N6xy/r2S2+qNffdYy/T84B9OOLOq/HUuKnYWkTBH6sY/+J9zL7/OQpXrCGpRxeWfTqHYx+6vvEMg89egFF4Br8YhWeoj25AZXKCs9XXh3UkbFdCEY25EvK6LVcE+equam5ZTVlXV+xyF8+NSS6bNWhIdmVSUg+wZiY0BVd5BZt+XcqYmyYB4IyMIDopAa/bg6rirq7GERHBwjc+Yc9TjsQREZbmpeUbexs6LEbhGeojEyhPiG+34zlhIzVeUhpzJaRuayJQYZSzyTNCArXqPF71LHXLxuldescu361vujgcLfp4Kd24hZiURGbf+yxbV6whY+BuHHTlBHqO3If3LriJbsP2ICo+li2L/2TYxLDt1NUlXAUb2j5G4Rl2YVxOchSQGBkhxXExjlZbEN2RacyVkHrECVAcF9skH3ie/D+2nu2ZnZKdUFOvVVfschfPiUkpnTN4SJeKxMRmWXP+UI+Xgj9WMerqiWQN6c93j77Eglc/YsSFpzL0zL8BMPv+5xh+/niWfjyTdT8tIq1vz1ArPzPJylAv7Wa+sSGkJAPe3XpEJXZGD+fB4tDU0i6R2yqK/UZ6xOnxer2VCfEB+cBzu6oq+635qGyy46v07MiaXSameLxez28u1uam9d5606ijUj7f74BeFYmJQd2fMz4zjfjMNLKG9AdgtzH7U7B81fb4guWrQJXkXl1ZOfMHxt5+JaUbtlCydlMwxWgMY+EZ6sVYeAZ/JAPau3ukGb9rAbYrodKvPf09OB07Kyl1UFRTWYQzLr2xfLZbdfG7WnVFLnfRnJiU8rmDh2RXJCa26hZwcekpJGSlU7xmAym9urH+599J7bNjFUbe828zesqF1pie13L+ICK4q6vry7I1MArPUC9G4Rn8kQxIl4zItrZXYrsjK0kyBm9evXpp1m47LVVwiMNTYPnAq1fhuV1VlYM2fek+Pn7dTmN1Hq/Xs9jt2DC9a5/4FX37pQEh85R+0FXnMOPOp/DWuEnslsWYf1oTWFbNzSNjUF/iMyxR0vv35u2JN5Derxfp/RtdpRFMYibNfS3l2dFn+resDZ0ao/AM/kgFJC7W0eL1YQbYN9u9iyshB053gZN6F8H6s+qKXO6i2bGWNVfZytZcfWQM6MPJz921S3if0cPpM3r49t8jLz+LkZefFUrRfOkCGIVn2AWj8Az+yAJqIiPMlmLBoq4rIac4PcVxMbt8UNS16txer3uJ27ExHNZcO6YLsDTcQhjaHqZBM/gjGvBERBAyd9UdndhoiT2oeO2m2VH9EhwOB06H01WWEL/T4nRfq66w2l00Oz61fO6A3btUJYTHmmvHmK54g1+MwjP4IwrwGgsvuPRIoUuXFWvLtvTtneiCKo2KioYdVt2RMWvSfvM4Nr/Udbeov3bra6y55mOeW4NfzINh8EcUoBFOMRZekBnTpzrxvfUllaXi8KLWbimHlc+MW5YQ47lxjwPdVQkJZpZhyzHtmsEv5sEw+CMS8EYYCy/oOJ3C4Un5zlfXuqle/3VhTRdX4n8PGBMFNLo9mCFgzHNr8It5MAz+sBSesfBahbQkjRoas75H9oiu0SKRimtzjcPh8ED9szYNjaOqjpioiIoaxBtuWQxtE6PwDP6IBLxOp5m00lrsO2h5zJai/TUmKtIJOLHq3NByos1aGkN9mK3FDP6IArwul7rCLUhHJSHOLcWlfxWEW44OijvcAhjaJkbhGfwhABVV3ma7rjE0Tu8uvxqrrnUI6V5mhvaDUXgGf1QAzm2VRuG1JumpxanFpQUbwy1HB6Qw3AIY2iZG4Rn8UQZEbqswCq+1iYv51UywCD5bwy2AoW1iFJ7BH2VARNk2o/Bam55dV3SrqKoqDbccHQyj8Ax+MQrP4I8yIKK03GMUXivjdIjU1CwxXXDBQzFdmoZ6MArP4I8yIKK41FMZbkE6A727Lsx2ezxmZmFwKOk26nJPuIUwtE2MwjP4owJgS6G7ItyCdAbiYl2xpWVrN4Rbjg6CWephqBej8Az+qAK8azfWbHPVmLV4oSArfX58uGXoIKwMtwCGtotReAZ/bKs9KCnzmC/mEJCVtjW9pKxoc7jl6AD8EW4BDG0Xo/AM/tiu5AqL3WbGW4iIjvq1JtwydACWh1sAQ9vFKDyDP4rs/47NW93GwgsRvbv+0a2yqro83HK0c4zCM9SLUXiGXZg2o8QDbAZi122qMQovRDidOKpdy0x9twyj8Az1YhSeoT7WALEr1lSbBjiE9O66IMvj8Zpp9c1AVV3A6nDLYWi7GIVnqI9VQOzvf1YXelWNn7YQER9XHVdSvm59uOVoj4jIQrMGz9AQRuEZ6mMzIBWVXndpmddMXAkhmSm/GO/nzWNeuAUwtG2MwjPUx1ZsD9xrNrpMN1EIyc7YkllaXrIl3HK0Q4zCMzSIUXiG+qhdE+ZYsqJ6VTgF6YxEOhcan25Nxyg8Q4MYhWfwy7QZJdXACiDxm5/LV4VZnE5H727LulW7XGZrtwBR1U3dRl2+KtxyGNo2RuEZGuIXIGn1+prykjKPGccLIRER6qyoWm66NQNERIx1Z2gUo/AMDfFn7UG4xvE8HmXfE5dx/CRri0RV5eaHNzLwqCXsfsxSHnspH4B3vyhmj+OWMvrMP9laZDkeWLGmmtOvXhUOsYNC7y4LMr1er3EQGxgzwi2Aoe1jFJ6hIdbY/8M2jvfoSwXs3i9m++8X3yti7cYaln42mCWfDeaM41IAePyVAn56ZyCTTk/ntY+tjWJueWQTd13dNRxiB4WE+Mr44rKNZolCYHwUbgEMbR+j8Az1Yo/jrQQSv/l526pQl79uk4tPZpVy4fi07WFPv17Avy7PxuEQALLSIwFwiFDt8lJR5SUyQpibV06XjAgG9IkOtdhBJS15flS4ZWjreLzexd1GXW5mEhsaxSg8Q2P8AiStWucqyy90h9Rn29X3bOCBKV23KzeAFWtdvPlpMcNPXs4xF67kj1XWZMZ/Tspi7Hkr+WhmKX8/PpU7n9rM/12WHUpxW4VumZuyy7aVttpuN5PveYW9j7+RnAl3bw/7/Y91/G3SQxx+zt1MvP4ZyrZZfoB/WriCsRPv4ZgL7mflWmt4saSsgr9f8wTh7Hl1OhwfhK1wQ7vCKDxDY2wfx1uwpPK3UBX68cxSstIi2G/PuJ3Cq11KTLSQ995ALjotjfNvWgvAEaMS+fm9gXz0zG58+HUJxx6SxPJV1Yy/chUX3bKWisr2OxTmkEWtNlvztGNH8uq/L98pbMr9r3HTJSfw9Us3c8wh+/D0a18D8OwbM3jpwUu5/crxvPzBNwA8OvVzrphwJA5HWJuSj8NZuKH9YBSeoTFWAtVA1MezSn8L1TZj387fxrQZpfTJWcwZk1cz4/tyzr5uNT2yIzn5iGQATjoimYXLKnc6r6LSy4vvFXH5WRnc+vgmpt7Xk4P3i+fVj4r8FdMu6NN9abdqV01l4ymbzsih/UlJ2vmjYuXaLYwc2h+A0SMG8+nsBQBERDiprHJRWe0iMsLJqvX5bNhSzEHDBraGaAHh9Xq3Aj+ETQBDu8IoPEODTJtR4gbmApl/rXWVbcp3r2nsnGBw77VdWTdnCKtmDOGN3N7kjEzglYd6c+LYJGb+YHnQmf3jNgbWGaN78PktXHlOBpGRQmWVIiI4hHZt4UVGeCMqKv8M2RKFgbt15Yu5CwH4eOZ8Nmy2Phb+MeFIrrrrZR5/eTrnnnII9//nI66/6PhQieUXEXm726jL2+/NNYQUo/AMgfAT4ASY/3vFonAKcuPF2bw7vYS9/raMf+Zu5L9399wet2FzDT8urODEsZYFeMXZGYwYv5xn3tjKmX9LDZfIQaFn9i+pXm9orOvcf57F1PfncvT597OtoprISCcAew7owcf/uY53Hr+KNRu2kpWejKpyyb/+xxV3TCW/sDQU4u2EiLwQ8kIN7RYxG+EbGmNcTrIT+DdQ1SUzQp6+vcd1ToeYj6UQ8/ufx69LTe7aI9j5rt24lYnXP8OMl2/eJW7Fms1ceedLfPLclO1hqsqZk5/kqdvP4/8efpsbJ41j7catzP5xCTdOGhds8eqlpsa9vPeYqwaFrEBDu8c0WoZGsR3CzgYyNuW7K9dtrFkRbpk6I8kJv0SEopyCojIAvF4vj079ggknHLxT/Nuf/0DOgXuQmhRPZZXL6jZ2CJXVNaEQbztOp+M/IS3Q0O4JyQtk6BDMB/4G8N0v237t3T1qQJjl6XT06LK+y/JV5YUJ8QlpjacOjMtufYF5C/6gsLic/U66hesuOJZtFdW8+N4cAI49dCinHzdye/rKKhdvffoDrz/8DwAuPiOHc6Y8RWREBE/cdm6wxGoUr6rb4XC8FLICDR0C06VpCIhxOckCPAAQGSGVU+/veWVCvDM5zGJ1Opav2mt1QvzI3uGWI9y4atyf9hlz1XHhlsPQvjBdmoaAmDajRIHpQEaNW73zFlR8H26ZOiN9ui/u5qpxd3rXQVGREbnhlsHQ/jAKz9AUvgfcQORLHxTOr3Z5q8ItUGcjKtITWb5t5cZwyxFOqqpdi7uNuvzrcMthaH8YhWcImGkzSsqAr4DskjKva8GSyp/DLVNnpHv2/FTtxGMRIo47wi2DoX1iFJ6hqczAem4cr35U/IPHAbtu6QAAEeFJREFUo55wC9TZSE0qSy4u3RLSfU3bClXVNeuioyLeDrcchvaJUXiGJjFtRkk+1lZOWavWucqW/VUdsv01DTtIjF8gjafqeKjqPWZnFUNzMQrP0By+AGIA3v6s+NtQ7a9p2EGP7NXdtlVUFIdbjlDiqnEXxsZEPR9uOQztF6PwDM1hNbAMSP/598r8ZSurw7rdWGfE4RC83t9Lwi1HKKl21dzabdTlrnDLYWi/GIVnaDL2EoVpQBLAf9/aOsPtUXd4pep89O7+Wxe329MpFEB5RdXqxPjYp8Ith6F9YxSeobksBpYAWX+sdpXM/63SuGgJMTFR7ujSbX91iiUK2yqqrzJjd4aWYhReiBCRWSIy3D7+VERS7L/LfNJ0E5F3mpH3bSJSISJZPmHlwZHcP7aV9yYQBzieeq1gbmWVd1trlmnYla4ZvyR19CHUopJtP+57wk0fhlsOQ/vHKLwwoKrHqmoxkAJc5hO+QVXHNzPbAuDaYMgXKNNmlPwFfAd0KSzxVH8+t+yrUJZvgPSU4tSSsq0d1srzer1et8dzUbjlMHQMjMJrJiLSR0SWisirIrJERN4RkTgROVxEfhGRRSLyPxGJ9nPuKhHJAO4D+onIAhF50M7zNzuNU0QeEpHfRGShiFxhh98nIovtsId8sv0fcLqI7LKxsIicLSI/2uU8a+d9qojk2vFXichK+7iviHzbSFm+vI/1HEW9+F7hgi1ba9Y3u1INzSIuZkGH7erbWlz+1j5/++fCcMth6BgYhdcyBgFPqeruQCkwGXgROF1V98LyRnFpA+ffCKxQ1aGqOqVO3MVAH2Coqu4NvCoi6cBJwB522F0+6cuxlN5VvpmIyO7A6cAoVR0KeICzsLyYj7aTjQa2ikh3+3hOI2Vtx16X9wHQVRVeeLfwk1A5KjVY9Oy6sltFZWXova+2MhWV1SWqenG45TB0HIzCaxlrVfVb+/gV4HDgL1VdbodNBQ5pZt5jgWdVrdmPqloIlABVwPMicjJQUeecx4CJIpLoE3Y4sB/wk4gssH/3VdVNQIKdtifwmi3raCxl2FhZvnwJFAKJ386v2DhvQcXcZl6zoRk4HSI17iVF4ZYj2KzZuHXy0HE3lYVbDkPHwSi8llHXkmnVhcC28tsfeAc4Hvi8TnwxluK63CdYgKm2FTlUVQep6m123HfAeVhr6motvgOBbxsry5dpM0qqsZR7BuDI/V/+7PxCd6fc+ipc9Om2KNvt8YTWA2srsn5T4ZycCXf/L9xyGDoWRuG1jF4icqB9fCaQB/QRkf522AQsT+H1UQYk1hP3JTBJRCIARCRNRBKAZFX9FLgG2MfPebnAJHY49/0aGF87g/P/27v34Kiu+w7g37MvSeixeiBkSSAwIWBjOeAUP2Inwdn6EYd4y8SxHUidOrX/aJt43HRMOvUkfUym7RC7tV3XHewabFwTE4NtUCCYJV4raClvkLRGQiCBJPR+rySkfZ/+cS6xhmK9rN27q/v9zGgQq733/mBG+uqe+zvnaOe5sp9aBYBnABwEcBrANwAEpJS+SV5rLC+AcgDFobCMvrS1+/1wWM6aH8CJLi01mDo41Dwrfsm4PBIYbmrreSRe1xvbQX3V648LIf4zXnVQ7DHwPp86AD8SQtQCyAHwAtQd0w4hhBdAFMCmzzpYStkL4JDWmPLcVV9+HUAzgGohRBVUoGYC2COEqAbggXpmePU5e6AaSVK0v9cA+BkAl3bcAQCF2tsroIYzD0opIwAuaefFZK411phpCv0Asqvr/L2uQ0MHxjuGZlbB3FOf9ctT0pBS4lxj+0+++9RLnfG4nhDCHI/rUGLgjufTJIRYBGCPlLJU51ISitNh/yJUwF4CEH7574u/v7DItmSCw2iG1F54qMOemXud3nVM19kLbbsdj/3z2sm8VwixAWpE4j+EEC8AWCGldAghHACeALAHwLNQw/p7pZR/qx03DOBVqOfkP4JqyHpGSnlCCPFDAH8H9XiiSjv/j4UQb0I1pq0CcB2An0opd46p4xGoXzI/kFL+wyRq+wGAzdr5JIAtUsoXPs//HU2Md3g0o8rcvvMAdgOYDwDPvd612x+IjupblXGk2qqSdom31s6+pm1lh9ZP4ZCxncaroJqwrNpr5wBsBOAAsBLArUKIK0GaDuColHKFlPLKiAaEEIUA/gnAXQC+CmD5Vdcr1F7/NtSUIggh7gPwRajn3SsB/JEQ4usT1HZQe2+xlLJU6+h+Ywr/bpomBt40SSkbeXf3mfZALTCd39wWGt76Qf/OaFTO2rliiaSksL541O9Pus7G4RH/5fKjNWs37ygfrxv4aiehAiYLQADAYahw+RrUHVq5lLJba8Dahk87piMA3rvG+W4fc0wQaoh+rF1Syqj2mKBAe+0+7eM0gFMAboAKwPFqqwBwAcBiIcTLQohvQt09Uowx8GjGlbl9QQCvQW0hlLq3fPDCvoND+3QuyxDMZohgsK5X7zqmIhKJRj0n6n6yYeM7lVM5TkoZAnARwONQHccVUI1XSwA0jnOoX3tmPVWBMZ+LMX/+65gu6CVSys0T1FYrpeyHagQrB/AXUM/sKcYYeBQTZW5fK9Q3cREA86vbe0+crhk5pnNZhlBSWDUvEokmzU70J89c/NX2vYen+wN/bKdxBVR4nAZwDMBqIcRcrTFlHcbvmAbUxsarhRB52vDjw5O4/n4Af651NUMIUTxmTdtr1iallNpKSyYp5XtQz7y/POl/MU0bA49i6QjU87wSAOIXr3R+2NwWrNe5plkvfU5gjm+oJSmWePPWNR/9r20HnnR5vNPtnquAerZ2WErZCbVYQoWUsh1qJaOPoZpPTkopx12AWjvmH6GGHw9B7QYyLimlC2ru62GtM3snPp1qdM3atK8VAyjXFoN4G6pRhmKMXZoUU06H3Qw1L3AVgOZcuznlhWeLnsixW/J1Lm1W6+zN74lE187Vu47xnLvYXv/85r137fn4dJfetZAx8A6PYqrM7YtArfHZBG1XhX/Z1PUrfyA6leYEmqKCvO65vqGBhA2SS+297Zt3lDsZdhRPDDyKuTK3zw/gZaghnZy6i4GBl7Z2vx0IRv06lzar2azVgYnfFX/dfYP9b+2qePh/dnsmHDIkmkkMPIqLMrevD8CLUHOg0g+dGmn/9y3dWwNBztGLlYVF54r9gWBCbco7MHh5aFvZoSdeefvAoYnfTTSzGHgUN2VuXyPU8mt5ADIOV450/NuW7rc4MT02LGZp8vvruvWu44q+gWHfq9s/erqytmmX3rWQMTHwKK7K3L4zAJ6HWns040jlSMfzm7u38plebJQUVuVHo1HdJ/139w32v/TWh8/WNrRt/RwdmUSfCwOP4q7M7avBmNA7Vj3S+cv/7to66o8m1PDbbJCRPpo+MNSm6xSFjp6B3hff3PfTptaeTS6PV/fwJeNi4JEuyty+WgDPAcgGkHnik9Guja91bR0ZjSbdsliJLs9+KkWva7d19ne9+Ma+v27t7N/MsCO9cR4e6crpsC8DsAFqh/WhRfNtmT//q4Lv5edainQubVY5e/HRnqyMrLjOy6ttaL246Z3fbfANjb7PYUxKBLzDI12VuX11AH4J1b05t7ElOPTUL1rfqLvo9+pc2qxiMXnj1hgkpYT7yJnKja/95scMO0okvMOjhOB02IsBPA31XK8VAP7mh/lfXX1b+h8LIcY9liYWCpvCLZ0/CKXYrGmxvU4k9O5vj1QcOOT9GYAjDDtKJAw8ShhOhz0TaoHdm6B2e48+dJ992boHc75jswqbvtUlvzP1dzXm2JcvitX5h0f8w69td++prmv+ucvj5ZqplHAYeJRQnA67FWr36Puh7vQCq0rT5j39Z/mP2jPNufpWl9wGh9OHBi+vyzCZZv6Wub6ps/nV7R+92903uNHl8fbM9PmJZgIDjxKO02EXUJt1Pg6gD8BQZrrJ+swT8+5ZeWPqbRzinL4z9WtacuxF82fqfOFwJPTb31eefN91fBuAzS5P/J4VEk0VA48SltbB+RQAG4B2APKB1ZnXP/YnOWsz5piz9K0uObV2FnUI05rrZuJcPf1D3Zve+Z2nvqlzE4CPXB5v0uzBR8bEwKOE5nTYswH8KYBbAXQAGM3LNqdseHLeA8uXpK7Qt7rkdK5xXW9GekbedI+PRqU8Vt3wyZad5R8GQ+FXXB5v00zWRxQrDDxKeNoQ5+1QQ5wC6m4PD91vX/bIA9kPpqWa0nUsL+mca7y5KSP9joXTObar19f2xnsHT9U2tO4E8C6HMCmZMPAoaTgd9jyo0PsSgDYAgYI8S9pfrs9bveLGtFvNJsF5pZMQDJlDbd2PRWxWa+pkjwkEQ6P7K6pPfuA6Xi2B1wFUcsoBJRsGHiUVp8Nugmpo+T6AKNQwpyxdmpr75MO59y5ekHKDrgUmiTP1X2/MsS9bNNH7pJSoaWit3bKjvKp3YHgfgF0uj3cw9hUSzTwGHiUlp8NeAOC7AG4DMAigFwDuuTOjZN23c+7n0mTjGxjMGLzs/16mGKfltamtp/7Xew/X1NS3VgN40+XxNsSxRKIZx8CjpKU921sCYD2AxQC6AQwLAax/MKd0zerMezLSzXZdi0xgZ+qdrTn2guKrX2/t7LuwY9/RTyprmzoA/BrAQZfHG45/hUQzi4FHSU8b5vwy1DBnNtQwZ8BmFaZHv5V90923Z3wlP9dSqGuRCehS+4J2s+Wbf/h/6egeaHrfddx7rLqhC4ALwH6XxzugX4VEM4uBR7OG02FPAXA3gO8AsELd8Y0CwL13ZSxcc3fWHYvm25aZOHMdABCNSpxvXt/b2TPc9hv3qfqqs80DANwA9rk83l696yOaaQw8mnWcDnsWgDsBrAGQAWAAavshLF+SkvPot3LuKF2aeovVIqw6lqmr/kEZqKyRR9/abWpp7YwEARwEsNfl8XbpXRtRrDDwaNZyOuw2ACsAOAHMh7rb6wYg5+aYU9fea1++qnTOlwrzLQuNcNMXjsjwpfZgwwHPcP2uj2yLw5G0/wXEfgC/Z9CRETDwaNbTmluWQi1IfQvUdIYeaMOdS0psWWu+kVV689K05fm55uLZFH6hsAw2tgTPH/eOnN1bPugbuhw1AeiMRFM9ofC8j10e72W9aySKFwYeGYrTYS+EWrXlbgB2ACGo8AsCwBcW2LLu/1rmDaVLU5cVzLUuSMZhz0AwOtrQHKw7UjVybv/BQd9oQFqgQv4YgI8BNJS5fVF9qySKPwYeGZLTYTdDTWW4Hep5XyqACNR8Pj8AWC3C9JWVcwpXLk8r+cIC28LCedaS1BRTTDdQnY7B4Uh/e1eo5UJLsKWyZrTjuHckEo7ADBXmJ6CC7lyZ28dlwMjQGHhkeE6H3QLgeqjhzjsBXNmJIQTV8DIKAEIAt9yYlr/q5rSShUW2wtxsS152pjl3TprIiscwqJQSI3455BuM9F7qCLaevRC4dLRqpLOlI2QGkA5AAggAOAzgJID6MrcvGPPCiJIEA49oDO15Xz6AEgA3Qq3bmQcVJhKq23MUwB8mYmemm6w3LE7JuX5+Sl5RgSV3Xp4lL2OOOT3FKlKsVpFiswqb1SJSrBaRYrEIy9jrRaIyGonIUDiMUDgiQ+GIDIVCMjh0OTrYNxDp7+oLD7R3h/qbWoMD55sCvlG/TIEairXg04CrA3AGQCOAxjK3j5PEia6BgUc0AW2LohKoxpebABRC7dEXhdq9QUANg45ABVBE+9r/Y7UIkz3TZItEEB26HAmFI7j6G9ACNbyapv0prrpOO4BPAJwHcAlAN5/HEU0OA49oirS7wAwAuVB3f3MBLARQrH2eCsAMdQc2mTASYz5GAXRBBVub9vkAgH4AAxyiJJo+Bh7RDNMC0QwgRfuwjflcQD0bvNZHGEC4zO3jNyVRDDDwiIjIELhhJhERGQIDj4iIDIGBR0REhsDAIyIiQ2DgERGRITDwiIjIEBh4RERkCAw8IiIyBAYeEREZAgOPiIgMgYFHRESGwMAjIiJDYOAREZEhMPCIiMgQGHhERGQIDDwiIjIEBh4RERkCA4+IiAyBgUdERIbAwCMiIkNg4BERkSEw8IiIyBAYeEREZAgMPCIiMgQGHhERGcL/ARZKHWE/2RboAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}