cap4_fundamentos_de_textos.ipynb

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  "metadata": {
    "colab": {
      "name": "text_preprocessing.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNdf/se1jDkqVnUHihfxU0e",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/viniciusrpb/datavis_book/blob/main/cap4_fundamentos_de_textos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MqWuzM3RGEz_"
      },
      "source": [
        "# Capítulo 4: Fundamentos de Textos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-f2wlzmX4LB"
      },
      "source": [
        "Instalação do nltk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlTS1QgspKhj",
        "outputId": "134a1566-3524-45c8-aec8-9e3a187e5b93"
      },
      "source": [
        "!pip install nltk"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgRJx9tKONBJ"
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      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpMExxSY1PJ8"
      },
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords,reuters\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DDGdvrdOduI"
      },
      "source": [
        "Download dos resouces necessário para o uso das técnicas de pré-processamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4adCQAN1zM4",
        "outputId": "0178af18-c9a1-4d3d-b749-f1bdfdd0f514"
      },
      "source": [
        "nltk.download('punkt')\n",
        "nltk.download('reuters')\n",
        "nltk.download('stopwords')\n",
        "stopwords = stopwords.words('english')\n",
        "\n",
        "!unzip /root/nltk_data/corpora/reuters.zip -d /root/nltk_data/corpora/"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALN1Nzc_OraY"
      },
      "source": [
        "Pegamos os IDS dos documentos para o conjunto de treinamento e de testes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAMr82ov1RwT"
      },
      "source": [
        "documents = reuters.fileids()\n",
        "\n",
        "train_docs_id = list(filter(lambda doc: doc.startswith(\"train\"),documents))\n",
        "test_docs_id = list(filter(lambda doc: doc.startswith(\"test\"),documents))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkOoO7_bO2YH"
      },
      "source": [
        "Recupera os textos originais a partir dos IDs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdMyeZkVO0NF"
      },
      "source": [
        "train_docs = [reuters.raw(documento) for documento in train_docs_id]\n",
        "test_docs = [reuters.raw(documento) for documento in test_docs_id]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZYGK1rOO7Ne"
      },
      "source": [
        "A seguir, imprimir os cinco primeiros documentos (notícias) do corpus da Reuters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2D5nmSl7ASY",
        "outputId": "1185eded-6c95-4035-fd74-a510bc97f95d"
      },
      "source": [
        "for doc in range(0,5):\n",
        "  print(train_docs[doc])"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BAHIA COCOA REVIEW\n",
            "  Showers continued throughout the week in\n",
            "  the Bahia cocoa zone, alleviating the drought since early\n",
            "  January and improving prospects for the coming temporao,\n",
            "  although normal humidity levels have not been restored,\n",
            "  Comissaria Smith said in its weekly review.\n",
            "      The dry period means the temporao will be late this year.\n",
            "      Arrivals for the week ended February 22 were 155,221 bags\n",
            "  of 60 kilos making a cumulative total for the season of 5.93\n",
            "  mln against 5.81 at the same stage last year. Again it seems\n",
            "  that cocoa delivered earlier on consignment was included in the\n",
            "  arrivals figures.\n",
            "      Comissaria Smith said there is still some doubt as to how\n",
            "  much old crop cocoa is still available as harvesting has\n",
            "  practically come to an end. With total Bahia crop estimates\n",
            "  around 6.4 mln bags and sales standing at almost 6.2 mln there\n",
            "  are a few hundred thousand bags still in the hands of farmers,\n",
            "  middlemen, exporters and processors.\n",
            "      There are doubts as to how much of this cocoa would be fit\n",
            "  for export as shippers are now experiencing dificulties in\n",
            "  obtaining +Bahia superior+ certificates.\n",
            "      In view of the lower quality over recent weeks farmers have\n",
            "  sold a good part of their cocoa held on consignment.\n",
            "      Comissaria Smith said spot bean prices rose to 340 to 350\n",
            "  cruzados per arroba of 15 kilos.\n",
            "      Bean shippers were reluctant to offer nearby shipment and\n",
            "  only limited sales were booked for March shipment at 1,750 to\n",
            "  1,780 dlrs per tonne to ports to be named.\n",
            "      New crop sales were also light and all to open ports with\n",
            "  June/July going at 1,850 and 1,880 dlrs and at 35 and 45 dlrs\n",
            "  under New York july, Aug/Sept at 1,870, 1,875 and 1,880 dlrs\n",
            "  per tonne FOB.\n",
            "      Routine sales of butter were made. March/April sold at\n",
            "  4,340, 4,345 and 4,350 dlrs.\n",
            "      April/May butter went at 2.27 times New York May, June/July\n",
            "  at 4,400 and 4,415 dlrs, Aug/Sept at 4,351 to 4,450 dlrs and at\n",
            "  2.27 and 2.28 times New York Sept and Oct/Dec at 4,480 dlrs and\n",
            "  2.27 times New York Dec, Comissaria Smith said.\n",
            "      Destinations were the U.S., Covertible currency areas,\n",
            "  Uruguay and open ports.\n",
            "      Cake sales were registered at 785 to 995 dlrs for\n",
            "  March/April, 785 dlrs for May, 753 dlrs for Aug and 0.39 times\n",
            "  New York Dec for Oct/Dec.\n",
            "      Buyers were the U.S., Argentina, Uruguay and convertible\n",
            "  currency areas.\n",
            "      Liquor sales were limited with March/April selling at 2,325\n",
            "  and 2,380 dlrs, June/July at 2,375 dlrs and at 1.25 times New\n",
            "  York July, Aug/Sept at 2,400 dlrs and at 1.25 times New York\n",
            "  Sept and Oct/Dec at 1.25 times New York Dec, Comissaria Smith\n",
            "  said.\n",
            "      Total Bahia sales are currently estimated at 6.13 mln bags\n",
            "  against the 1986/87 crop and 1.06 mln bags against the 1987/88\n",
            "  crop.\n",
            "      Final figures for the period to February 28 are expected to\n",
            "  be published by the Brazilian Cocoa Trade Commission after\n",
            "  carnival which ends midday on February 27.\n",
            "  \n",
            "\n",
            "\n",
            "COMPUTER TERMINAL SYSTEMS &lt;CPML> COMPLETES SALE\n",
            "  Computer Terminal Systems Inc said\n",
            "  it has completed the sale of 200,000 shares of its common\n",
            "  stock, and warrants to acquire an additional one mln shares, to\n",
            "  &lt;Sedio N.V.> of Lugano, Switzerland for 50,000 dlrs.\n",
            "      The company said the warrants are exercisable for five\n",
            "  years at a purchase price of .125 dlrs per share.\n",
            "      Computer Terminal said Sedio also has the right to buy\n",
            "  additional shares and increase its total holdings up to 40 pct\n",
            "  of the Computer Terminal's outstanding common stock under\n",
            "  certain circumstances involving change of control at the\n",
            "  company.\n",
            "      The company said if the conditions occur the warrants would\n",
            "  be exercisable at a price equal to 75 pct of its common stock's\n",
            "  market price at the time, not to exceed 1.50 dlrs per share.\n",
            "      Computer Terminal also said it sold the technolgy rights to\n",
            "  its Dot Matrix impact technology, including any future\n",
            "  improvements, to &lt;Woodco Inc> of Houston, Tex. for 200,000\n",
            "  dlrs. But, it said it would continue to be the exclusive\n",
            "  worldwide licensee of the technology for Woodco.\n",
            "      The company said the moves were part of its reorganization\n",
            "  plan and would help pay current operation costs and ensure\n",
            "  product delivery.\n",
            "      Computer Terminal makes computer generated labels, forms,\n",
            "  tags and ticket printers and terminals.\n",
            "  \n",
            "\n",
            "\n",
            "N.Z. TRADING BANK DEPOSIT GROWTH RISES SLIGHTLY\n",
            "  New Zealand's trading bank seasonally\n",
            "  adjusted deposit growth rose 2.6 pct in January compared with a\n",
            "  rise of 9.4 pct in December, the Reserve Bank said.\n",
            "      Year-on-year total deposits rose 30.6 pct compared with a\n",
            "  26.3 pct increase in the December year and 34.5 pct rise a year\n",
            "  ago period, it said in its weekly statistical release.\n",
            "      Total deposits rose to 17.18 billion N.Z. Dlrs in January\n",
            "  compared with 16.74 billion in December and 13.16 billion in\n",
            "  January 1986.\n",
            "  \n",
            "\n",
            "\n",
            "NATIONAL AMUSEMENTS AGAIN UPS VIACOM &lt;VIA> BID\n",
            "  Viacom International Inc said &lt;National\n",
            "  Amusements Inc> has again raised the value of its offer for\n",
            "  Viacom's publicly held stock.\n",
            "      The company said the special committee of its board plans\n",
            "  to meet later today to consider this offer and the one\n",
            "  submitted March one by &lt;MCV Holdings Inc>.\n",
            "      A spokeswoman was unable to say if the committee met as\n",
            "  planned yesterday.\n",
            "      Viacom said National Amusements' Arsenal Holdings Inc\n",
            "  subsidiary has raised the amount of cash it is offering for\n",
            "  each Viacom share by 75 cts to 42.75 dlrs while the value of\n",
            "  the fraction of a share of exchangeable Arsenal Holdings\n",
            "  preferred to be included was raised 25 cts to 7.75 dlrs.\n",
            "      National Amusements already owns 19.6 pct of Viacom's stock.\n",
            "  \n",
            "\n",
            "\n",
            "ROGERS &lt;ROG> SEES 1ST QTR NET UP SIGNIFICANTLY\n",
            "  Rogers Corp said first quarter\n",
            "  earnings will be up significantly from earnings of 114,000 dlrs\n",
            "  or four cts share for the same quarter last year.\n",
            "      The company said it expects revenues for the first quarter\n",
            "  to be \"somewhat higher\" than revenues of 32.9 mln dlrs posted\n",
            "  for the year-ago quarter.\n",
            "      Rogers said it reached an agreement for the sale of its\n",
            "  molded switch circuit product line to a major supplier.\n",
            "      The sale, terms of which were not disclosed, will be\n",
            "  completed early in the second quarter, Rogers said.\n",
            "  \n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5ynik2ePT8e"
      },
      "source": [
        "A partir do texto original, realiza-se o pré-processamento dos textos fazendo a remoção de stopwords (da língua Inglesa) e transformação para uma matriz Term Frequency - Inverse Document Frequency (TF-IDF). Assim, cada documento será representado por um vetor TF-IDF.\n",
        "\n",
        "Observe que a geração dos vocabulário do corpus é feita apenas com o conjunto de treinamento - nunca inclua o conjunto de testes, caso contrário, o modelo vai aprender o vocabulário do conjunto de testes - o que significa um *cheating*.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEYAvDyq9LP2"
      },
      "source": [
        "tfidf = TfidfVectorizer(stop_words=stopwords)\n",
        "\n",
        "tfidf_training = tfidf.fit(train_docs)\n",
        "\n",
        "tfidf_training = tfidf.transform(train_docs)\n",
        "tfidf_test = tfidf.transform(test_docs)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itfScucpV_mR"
      },
      "source": [
        "Transforma o problema de classificação originalmente multilabel para um problema de classificação unária."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLFCdOoYV_45"
      },
      "source": [
        "mlb = MultiLabelBinarizer()\n",
        "y_train = mlb.fit_transform([reuters.categories(documento) for documento in train_docs_id])\n",
        "y_test = mlb.fit_transform([reuters.categories(documento) for documento in test_docs_id])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6aWxpnHW09m"
      },
      "source": [
        "Pegamos apenas o primeiro tópico como o atributo classe. Lembre-se que esse atributo é binário, em que o valor 1 indica a presença do tópico em um documento, e o valor 0 indica a ausência do tópico no documento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFE6E4jUW1Mu"
      },
      "source": [
        "oneclass_train = y_train[:,0]\n",
        "oneclass_test = y_test[:,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWA3AC7vBCD-",
        "outputId": "d9ddd554-46c4-468a-b4fa-077dbb68ec68"
      },
      "source": [
        "print(tfidf_test.shape)\n",
        "\n",
        "print(tfidf_training.shape)\n",
        "\n",
        "print(oneclass_train)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7769, 26147)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLtSYN-FXPZN"
      },
      "source": [
        "Cria o objeto da classe Suport Vector Classifier e treina o modelo utilizando os textos de treinamento, já transformados como uma matriz TF-IDF:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ndGE9w29rA0",
        "outputId": "1dd84579-50df-4982-8d3b-044725465387"
      },
      "source": [
        "clfsvm = SVC(kernel=\"rbf\",gamma='scale',C=1)\n",
        "\n",
        "clfsvm.fit(tfidf_training,oneclass_train)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wiZTH5ZBXXpj"
      },
      "source": [
        "Realiza a classificação utilizando os textos do teste, que está na representação TF-IDF:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynchIDBpAqNU"
      },
      "source": [
        "y_classified = clfsvm.predict(tfidf_test)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3W_4SgaXoCz"
      },
      "source": [
        "Apresenta os resultados da classificação no conjunto de testes. Observe o desbalanceamento!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3to7FQTcCk-a",
        "outputId": "590d9874-a450-4bc8-b20c-2cbad685f9cf"
      },
      "source": [
        "print(classification_report(oneclass_test,y_classified))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      2300\n",
            "           1       0.98      0.95      0.97       719\n",
            "\n",
            "    accuracy                           0.98      3019\n",
            "   macro avg       0.98      0.97      0.98      3019\n",
            "weighted avg       0.98      0.98      0.98      3019\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}