tipos_de_tarefa.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    },
    "colab": {
      "name": "tipos_de_tarefa.ipynb",
      "provenance": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjOuA2KcfY1q",
        "colab_type": "text"
      },
      "source": [
        "# Parte 3 do PP1 de RNA2020.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohDLtQqSfY1r",
        "colab_type": "text"
      },
      "source": [
        "## Tipos de Tarefas\n",
        "Recapitulem que dados fornecem experiência sobre um problema. No caso em questão, sugira:\n",
        "- [x] Uma tarefa de classificação mediante Aprendizado Supervisionado que poderia ser feita com\n",
        "esta base de dados. Qual seria o atributo-alvo? Quais métricas de desempenho poderiam ser\n",
        "aplicadas? Que tipo de validação seria apropriado?\n",
        "- [x] Uma tarefa de regressão mediante Aprendizado Supervisionado que poderia ser feita com\n",
        "esta base de dados. Qual seria o atributo-alvo? Quais atributos preditores a equipe considera\n",
        "relevantes para o cenário?\n",
        "- [x] Bônus: Qual tarefa de Aprendizado Não-Supervisionado poderia ser concebida neste contexto?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ro2k8sLwfY1s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import re\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import dates\n",
        "from matplotlib.pyplot import plot_date\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rp9C7LkHfY1w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b3eaaa7c-6eed-4126-be34-98a8ea97c76f"
      },
      "source": [
        "file = 'dataset_limpo_covid19_manaus.csv'\n",
        "df_dataset = pd.read_csv(file)\n",
        "df_dataset.dt_notificacao = pd.to_datetime(df_dataset.dt_notificacao)\n",
        "df_dataset"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>idade</th>\n",
              "      <th>sexo</th>\n",
              "      <th>bairro</th>\n",
              "      <th>conclusao</th>\n",
              "      <th>dt_notificacao</th>\n",
              "      <th>tipo_teste</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>ADRIANOPOLIS</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>RT-PCR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41</td>\n",
              "      <td>F</td>\n",
              "      <td>BETANIA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-03-27</td>\n",
              "      <td>RT-PCR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>CIDADE NOVA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44</td>\n",
              "      <td>M</td>\n",
              "      <td>SAO JORGE</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>F</td>\n",
              "      <td>COLONIA TERRA NOVA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-04-24</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6355</th>\n",
              "      <td>14</td>\n",
              "      <td>F</td>\n",
              "      <td>DA PAZ</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-06-25</td>\n",
              "      <td>TESTE RÁPIDO - ANTÍGENO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6356</th>\n",
              "      <td>5</td>\n",
              "      <td>F</td>\n",
              "      <td>REDENCAO</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-06-18</td>\n",
              "      <td>TESTE RÁPIDO - ANTÍGENO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6357</th>\n",
              "      <td>70</td>\n",
              "      <td>F</td>\n",
              "      <td>DOM PEDRO I</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>TESTE RÁPIDO - ANTÍGENO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6358</th>\n",
              "      <td>68</td>\n",
              "      <td>M</td>\n",
              "      <td>JORGE TEIXEIRA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>RT-PCR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6359</th>\n",
              "      <td>45</td>\n",
              "      <td>F</td>\n",
              "      <td>TARUMA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>2020-05-06</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6360 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      idade sexo  ... dt_notificacao                tipo_teste\n",
              "0        25    F  ...     2020-03-20                    RT-PCR\n",
              "1        41    F  ...     2020-03-27                    RT-PCR\n",
              "2        37    M  ...     2020-04-27  TESTE RÁPIDO - ANTICORPO\n",
              "3        44    M  ...     2020-04-27  TESTE RÁPIDO - ANTICORPO\n",
              "4        27    F  ...     2020-04-24  TESTE RÁPIDO - ANTICORPO\n",
              "...     ...  ...  ...            ...                       ...\n",
              "6355     14    F  ...     2020-06-25   TESTE RÁPIDO - ANTÍGENO\n",
              "6356      5    F  ...     2020-06-18   TESTE RÁPIDO - ANTÍGENO\n",
              "6357     70    F  ...     2020-02-06   TESTE RÁPIDO - ANTÍGENO\n",
              "6358     68    M  ...     2020-01-06                    RT-PCR\n",
              "6359     45    F  ...     2020-05-06  TESTE RÁPIDO - ANTICORPO\n",
              "\n",
              "[6360 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWtRxj52fY11",
        "colab_type": "text"
      },
      "source": [
        "## Visualizando alguns valores dos atributos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0eJJpP6fY12",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9d94eae9-6dc5-43d4-e607-f772b3914ab8"
      },
      "source": [
        "df_dataset.sexo.value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F    3605\n",
              "M    2755\n",
              "Name: sexo, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3BAXkJufY16",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "5a6e0d64-11aa-4927-e661-5141be25fe6e"
      },
      "source": [
        "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
        "labels = 'F', 'M'\n",
        "sizes = df_dataset.sexo.value_counts()\n",
        "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
        "\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOXEHsvUfY1-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "aa3c60bc-17ab-4239-9e55-e585c570b46b"
      },
      "source": [
        "df_dataset.conclusao.value_counts()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Recuperado    6347\n",
              "Óbito           13\n",
              "Name: conclusao, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pgns9r0TfY2E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "6088949c-fa63-4942-dfc0-0fe7281070e3"
      },
      "source": [
        "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
        "labels = 'Recuperados', 'Óbitos'\n",
        "sizes = df_dataset.conclusao.value_counts()\n",
        "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
        "\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4e2M4gUfY2H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "280624d6-6a13-4826-d423-9ccc10158ee6"
      },
      "source": [
        "df_dataset.idade.value_counts()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38    216\n",
              "39    214\n",
              "40    214\n",
              "46    212\n",
              "41    204\n",
              "     ... \n",
              "96      1\n",
              "97      1\n",
              "90      1\n",
              "94      1\n",
              "93      1\n",
              "Name: idade, Length: 97, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eX48Uo3AfY2M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a2f317ab-7b81-4eb0-8431-0b989a45b2d1"
      },
      "source": [
        "x = df_dataset.idade.values\n",
        "plt.hist(x, density=False, bins=100)  # `density=False` would make counts\n",
        "plt.ylabel('quantidade de pessoas')\n",
        "plt.xlabel('idade');"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K13aNvtifY2P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "be3f7b93-9e93-472e-963c-f9e82b5ec061"
      },
      "source": [
        "# Distribuição das idades por data de confirmação \n",
        "d = dates.date2num(df_dataset.dt_notificacao)\n",
        "plot_date(d, df_dataset.idade)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff91838c3c8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2Q32kT8fY2X",
        "colab_type": "text"
      },
      "source": [
        "## Propondo terefa de ***classificação*** para a base de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0hTLjIJfY2X",
        "colab_type": "text"
      },
      "source": [
        "### Tarefa: classificar se o \"paciente\" se recuperou ou não do covid-19"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyQZWzY1fY2Y",
        "colab_type": "text"
      },
      "source": [
        "Com base nos dados solicitados pelo projeto, uma tarefa de classificação que pode ser realizada em cima dos dados é: classificar um \"paciente\" como *Recuperado* da covid-19 ou evoluiu para *Óbito*. Um dos problemas encontrados para realizar esta tarefa é o desbalenceamento de clases, pois enquanto a quantidade de \"pacientes\" recuperados é 6347 o número de pessoas com conclusão igual a Óbito chega a 13.\n",
        "\n",
        "Uma possível forma de se contornar esse problema  do desbalenceamento das clases seria, selecionar outros atributos, pois observa-se que há bastante pessoas classificadas como Óbito, mas por causa da limpeza e escolha desses atributos do dataset atual essa classe diminuiu considerávelmente.\n",
        "\n",
        "Outra sugestão para aumentar a quantidade de classes minoritária seria considerar mais registros com campos faltantes e depois adicionar a média do atributo para este campo ou outra encontrar outra medida para preencher tais campos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQ9KkvyzfY2Z",
        "colab_type": "text"
      },
      "source": [
        "### Avaliação da tarefa de classificação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78hqB2e1fY2a",
        "colab_type": "text"
      },
      "source": [
        "Como se trata de uma classificação binária a acurácia é uma das principais métricas a ser considerada, porém para este conjunto de dados talvez não reflita a total realidade por ele apresentar quantidades de classes desbalanceadas, então a melhor maneira é adicionar outras métricas de válidação como por exemplo a precisão que diz quantos acertos para uma classe (true positives) sobre o total de classificados como positivos (true positives + false positives).\n",
        "\n",
        "Aliados a estas métricas citadas a cima, acredita-se que as seguintes também tornariam a avaliação mais eficiente para o conjuto de dados considerado: *recall*, *f1-score* e também a *G-score* (bastante usada em casses desbalanceadas)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9EeWKYwfY2b",
        "colab_type": "text"
      },
      "source": [
        "### Validação do modelo "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QawLLE4DfY2c",
        "colab_type": "text"
      },
      "source": [
        "Para a validação do modelo, ou modelos caso se use mais de um para esta tarefa, pensou-se em criar dois experimentos um usando a partição holdout e um outro usando o válidação cruzada. O primeiro para aferir se com os daos é possível criar um bom classificador mesmo que tenha classes desbalanceadas e o segundo para garantir que o/os modelo/os possa ser treinado/os e testado/os com todos os registros e assim contornar o problema das classes desbalanceadas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-iaUIDl2fY2d",
        "colab_type": "text"
      },
      "source": [
        "## Propondo uma tarefa de ***regressão*** para os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpqCCjAxfY2d",
        "colab_type": "text"
      },
      "source": [
        "### Tarefa: prever a idade dos \"pacientes\" com base nos demais atributos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFRqXkZefY2e",
        "colab_type": "text"
      },
      "source": [
        "Uma das tarefas de regressão pensada para este problema foi a previsão da idade dos pacientes, pois como se base a taxa de letalidade é maior nas pessoas idosas, mas será que com a base de dados disponível usando atributos como bairro, e sexo situação de conclusão do paciente e data de notificação e tipo de test se consegue acertar a idade dele? Nesta tarefa, acreditasse que os atributos tipo_teste e df_notificação não tenham tanha relevância, pois eles não são características dos pacientes, mas sim dos testes que eles fizeram. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4XZk8VMfY2f",
        "colab_type": "text"
      },
      "source": [
        "## Bônus: Qual tarefa de Aprendizado Não-Supervisionado poderia ser concebida neste contexto?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbGqdCSgfY2f",
        "colab_type": "text"
      },
      "source": [
        "Considerando o dataset limpo e preprocessado e com quantidade de atributos disponíveis. Uma tarefa de apredizado não supervisionado que poderia ser feita é o agrupamento de perfis de pacientes mais parecidos, ou seja, agrupar os pacientes com base nas suas caracteríscas. Uma meneira de realizar essa tarefa seria o usando o algoritmo *KNN* para realizar esse agrupamento e descobrir os perfis de quem se recuperou ou morreu por exemplo."
      ]
    }
  ]
}