1_Matriz_de_Indicadores.ipynb

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      "name": "1_Matriz_de_Indicadores.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/XavierCarrera/Tutorial-Machine-Learning-Clasificacion-Lineal/blob/main/1_Matriz_de_Indicadores.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "EzvU_FbgJumo"
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      "source": [
        "# Introducción\n",
        "\n",
        "Lo primero que veremos será inspeccionar como funcionan las matrices de indicadores y como sacarles provecho.\n",
        "\n",
        "Empezamos utilizando el data set clásico de [Iris](https://gist.github.com/curran/a08a1080b88344b0c8a7). En este, clasificamos tres tipos de flores: setosa, versicolor y virginca. Los features son los siguientes:\n",
        "\n",
        "* *sepal_length*: longitud del sépalo.\t\n",
        "* *sepal_width*: anchura del sépalo.\t\n",
        "* *petal_length*: longitud del pétalo.\t\n",
        "* *petal_width*: anchura del sépalo.\n",
        "\n",
        "Las librerías que utilizamos son:\n",
        "\n",
        "* **Numpy** que tiene precargadas funciones para manejar vectores y matrices.\n",
        "* **Pandas** que nos permite trabajar con matrices como tablas.\n",
        "* **Seaborn** y **Matplotlib** para visualizar datos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6ymBPGZzspT"
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      "source": [
        ""
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      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "LrualRTyIYRz"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (20, 10)\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQHcnf-06L8C"
      },
      "source": [
        "Empezamos abriendo el data set con la función *load_dataset* para visualizar nuestro data frame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
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        "id": "O8i5vBKwJ9pa",
        "outputId": "b4dc1539-4820-489a-8725-1ae8bb4ad38c"
      },
      "source": [
        "df = sns.load_dataset(\"iris\")\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>virginica</td>\n",
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              "    <tr>\n",
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              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
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              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
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      "metadata": {
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      "source": [
        "Con la función *describe* podemos ver la información estadística básica."
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "df.describe()"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.057333      3.758000     1.199333\n",
              "std        0.828066     0.435866      1.765298     0.762238\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKg0YxIa6bT1"
      },
      "source": [
        "Antes de pasar a las matrices, podemos usar la función *vale_counts* podemos contar cuantas flores hay en cada categoria."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPZlbuZRMzs4",
        "outputId": "18603b9a-4b8e-463f-90ce-13de40795fb7"
      },
      "source": [
        "df[\"species\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "versicolor    50\n",
              "setosa        50\n",
              "virginica     50\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UBHtjVlKqWh"
      },
      "source": [
        "# Creando una Matriz de Indicadores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDYp-mtR6kRV"
      },
      "source": [
        "Para poder crear una matriz de indicadores, lo único que tenemos que hacer es utilizar la función *get_dummies* y seleccionar la columna que queremos transformar en binario.\n",
        "\n",
        "A este proceso lo conocemos como *one hot encoding*.\n",
        "\n",
        "Hay que recordar que no lo estamos transformando en valores ordinales porque no hay relación relación ordinal entre los tipos de flores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "eeQ2ksUiKJpO",
        "outputId": "7ea85bcb-7980-4af2-e47d-5187474f3d5d"
      },
      "source": [
        "df = pd.get_dummies(data=df, columns=[\"species\"])\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0</td>\n",
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              "      <th>3</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>...</th>\n",
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              "      <th>145</th>\n",
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              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
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              "      <td>1.9</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>150 rows × 7 columns</p>\n",
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            ],
            "text/plain": [
              "     sepal_length  sepal_width  ...  species_versicolor  species_virginica\n",
              "0             5.1          3.5  ...                   0                  0\n",
              "1             4.9          3.0  ...                   0                  0\n",
              "2             4.7          3.2  ...                   0                  0\n",
              "3             4.6          3.1  ...                   0                  0\n",
              "4             5.0          3.6  ...                   0                  0\n",
              "..            ...          ...  ...                 ...                ...\n",
              "145           6.7          3.0  ...                   0                  1\n",
              "146           6.3          2.5  ...                   0                  1\n",
              "147           6.5          3.0  ...                   0                  1\n",
              "148           6.2          3.4  ...                   0                  1\n",
              "149           5.9          3.0  ...                   0                  1\n",
              "\n",
              "[150 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LdVLzx7NdcJ"
      },
      "source": [
        "# Evaluando la Clasificación Linear"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzpA6nT27DnD"
      },
      "source": [
        "Finalmente, podemos crear una línea de mejor ajuste para dividir a nuestros datos. En este caso, vemos tanto la longitud del sépalo como del pétalo para categorizar si pertenecen a virginica que es el valor 1 de la gráfica.\n",
        "\n",
        "Como podemos ver, no es la forma más eficiente. Por ello tendremos que usar una función sigmoide que veremos en el siguienet apunte."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "DJ5jxzMRNUIQ",
        "outputId": "ced8c004-7c9a-4545-dc3c-2f3c1116a7f1"
      },
      "source": [
        "sns.regplot(x=\"sepal_length\", y=\"species_virginica\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9798547f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "KzN9Cl1qOk0D",
        "outputId": "6777400d-68e9-4ab3-88eb-37fa40dae226"
      },
      "source": [
        "sns.regplot(x=\"petal_length\", y=\"species_setosa\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe978fd6b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}