A2_Introducción_a_Programación_Orientada_a_Objetos.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Introducción a Programación orientada a Objetos"
      ],
      "metadata": {
        "id": "cJcFfBNW2Yxt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En esta notebook  veremos **Introducción a Programación Orientada a Objetos**:\n",
        "  - Cómo definir una clase.\n",
        "  - Cómo inicializarla e usar los métodos y atributos.\n",
        "  - Qué es la herencia de Clases"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "## Ejemplo práctico\n",
        "\n",
        "> Consideremos la clase `Inmueble` con los **atributos** `ventanas`, `puertas`, `habitaciones`,  y `metros_cuadrados`. Además agreguemos el **método** `hacer_refacciones` que tiene por objetivo definir si un objeto de la clase necesita refacciones o no."
      ],
      "metadata": {
        "id": "ja5aJHP-WxBJ"
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    },
    {
      "cell_type": "code",
      "source": [
        "class Inmueble:\n",
        "  # Definimos los atributos o propiedades\n",
        "    ventanas = 5\n",
        "    puertas = None\n",
        "    habitaciones = None\n",
        "    metros_cuadrados = 100\n",
        "\n",
        "  # Instanciar el método\n",
        "    def hacer_refacciones():\n",
        "        refaccionar = True\n"
      ],
      "metadata": {
        "id": "LNdaDjusNrng"
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      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notar que los atributos o propiedades son compartidos por todos los objetos de tipo `Inmueble`. Definamos ahora un objeto particular `Casa` que es una **instancia de la clase Inmueble**."
      ],
      "metadata": {
        "id": "8Ab836jZ3pPh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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      "outputs": [
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          "text": [
            "Valor del atributo metros_cuadrados: 100\n",
            "Valor del atributo habitaciones: None\n"
          ]
        }
      ],
      "source": [
        "casa = Inmueble()\n",
        "print(f'Valor del atributo metros_cuadrados: {casa.metros_cuadrados}')\n",
        "print(f'Valor del atributo habitaciones: {casa.habitaciones}')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   **¿Qué sucede si quiero tener \"parametros de entrada\" que varien (es decir, que no estén fijos)?**\n",
        "*   **¿Cómo puedo usar o llamar al método hacer refacciones?**\n",
        "\n"
      ],
      "metadata": {
        "id": "O7umOzIc52Br"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### El método de inicialización\n",
        "\n",
        "Si queremos definir un objeto a partir de la clase anterior inicializarla:\n",
        "\n",
        "- El método de inicialización es único porque tiene un nombre predefinido, `__init__`, y no tiene un valor de retorno.\n",
        "- El programa llama automáticamente al método de inicialización cuando se crea un nuevo objeto a partir de la clase y permite acceder tanto a métodos como atributos.\n",
        "- La inicialización debe aceptar el parámetro especial `self` y luego todos las atributos de clase como parámetros.\n",
        "- El parámetro `self` permite que el método de inicialización seleccione la instancia de objeto recién creada.\n",
        "\n",
        "Inicialización de atributos***\n",
        "\n",
        "Cuando se inicializa un atributo se establece un valor por defecto.\n",
        "\n",
        "Por ejemplo, el primer atributo `self.ventanas = ventanas` establece la propiedad de `ventanas` del objeto creado para que sea igual al parámetro de `ventana` que se pasó en la creación del objeto."
      ],
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      "cell_type": "code",
      "source": [
        "class Inmueble:\n",
        "    # Define el método de inicialización\n",
        "    def __init__(self, ventanas, puertas, habitaciones, metros_cuadrados):\n",
        "        self.ventanas = ventanas\n",
        "        self.puertas = puertas\n",
        "        self.habitaciones = habitaciones\n",
        "        self.metros_cuadrados = metros_cuadrados\n",
        "\n",
        "# Crea un objeto de la clase Inmueble y establece\n",
        "# cada atributo con un valor particular\n",
        "mi_casa =Inmueble(8, 3, 5, 100)\n",
        "# Si muestro mi_casa simplemente me muestra \"el tipo de objeto\" que es\n",
        "mi_casa"
      ],
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        "outputId": "3981e9d6-4820-4acb-c89c-014153343819"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.Inmueble at 0x7d1264cd5ea0>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# En cambio si llamo a algún método en particular ahi puedo ver el valor\n",
        "mi_casa.puertas"
      ],
      "metadata": {
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        "outputId": "0e23cf7d-c6b2-4007-acc4-9bab6867b90e"
      },
      "execution_count": 4,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agreguemos ahora el método `hacer_refacciones` y otro método adicional que `precio_usd` que multiplica por 100 usd los metros cuadrados para obtener el precio."
      ],
      "metadata": {
        "id": "73vXYkHjw1yC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Inmueble:\n",
        "    # Define el método de inicialización\n",
        "    def __init__(self, ventanas, puertas, habitaciones, metros_cuadrados):\n",
        "        self.ventanas = ventanas\n",
        "        self.puertas = puertas\n",
        "        self.habitaciones = habitaciones\n",
        "        self.metros_cuadrados = metros_cuadrados\n",
        "\n",
        "    # Definir el método hacer_refacciones\n",
        "    def hacer_refacciones(self):\n",
        "        return True\n",
        "\n",
        "   # Definir el método precio_usd\n",
        "    def calcular_precio_usd(self):\n",
        "        precio_usd = 100*self.metros_cuadrados\n",
        "        return precio_usd\n",
        "\n",
        "# Crea un objeto de la clase Inmueble y setea cada atributo con un valor particular\n",
        "mi_casa = Inmueble(8, 3, 5, 20)\n",
        "#mi_casa = Inmueble(ventanas = 8, puertas =3, habitaciones =5, metros_cuadrados = 100,precio_usd =40) #Otra forma de hacer la linea anterior\n",
        "\n",
        "# Llamamos al método hacer_refacciones\n",
        "print(mi_casa.hacer_refacciones())"
      ],
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        "outputId": "566b27ee-c14f-468c-d558-06f98258d653"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Llamamos al método calcular_precio_usd\n",
        "print(mi_casa.calcular_precio_usd())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYDB3ixy0Ug3",
        "outputId": "40b502cb-5ca8-470f-ced5-f47763da61e5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Ejercicio:** Prueben cambiar el valor de atributo `metros_cuadrados` y correr nuevamente `mi_casa.calcular_precio_usd()`"
      ],
      "metadata": {
        "id": "ymcN_aXK3xZK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Herencia\n",
        "\n",
        "- La herencia permite que una nueva clase asuma las propiedades y comportamientos de otra clase. La clase de la que se hereda se denomina **clase padre**. Cualquier clase que hereda de una clase principal se denomina **clase hija**. Por ejemplo podriamos crear la clase `Departamento` (clase hija) a partir de la clase `Inmueble` (clase padre).\n",
        "\n",
        "- Las clases secundarias o hijas no solo heredan todas las propiedades y métodos, sino que también pueden expandirlos o sobrescribirlos. Expandir se refiere a la adición de propiedades o métodos a la clase hija que no están presentes en la clase padre. Sobrescribir es la capacidad de redefinir un método en una clase secundaria que ya se ha definido en la clase principal."
      ],
      "metadata": {
        "id": "Xc37MoLH4PBP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Departamento es la clase hija de la clase Inmueble\n",
        "class Departamento(Inmueble):\n",
        "    def __init__(self, ventanas, puertas, habitaciones, metros_cuadrados, numero_piso, en_venta):\n",
        "       #Hereda los atributos self, ventanas, puertas, habitaciones, metros_cuadrados\n",
        "       Inmueble.__init__(self, ventanas, puertas, habitaciones, metros_cuadrados)\n",
        "\n",
        "        #Expande Departamento para añadir el atributo adicional numero de piso y en_venta\n",
        "       self.numero_piso = numero_piso\n",
        "       self.en_venta = False\n",
        "\n",
        "    #Sobreescribe la función calcular_precio_usd agregando un parametro más en la multiplicación\n",
        "    def calcular_precio_usd(self):\n",
        "        precio_usd = 100*self.metros_cuadrados*self.numero_piso\n",
        "        return precio_usd\n",
        "\n",
        "    #Define el estado venta\n",
        "    def definir_el_estado_venta(self):\n",
        "      self.en_venta= True\n",
        "\n",
        "mi_departamento = Departamento(8, 3, 5, 10,2, False)\n",
        "\n",
        "print(mi_departamento.calcular_precio_usd())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSZNIbrm2Tw9",
        "outputId": "f669ffee-5658-4527-b83e-3d78f3629993"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mi_departamento.en_venta"
      ],
      "metadata": {
        "id": "VxVLesb83mFb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53dd53c8-19b2-4c43-9426-c92bbe20a1d2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mi_departamento.definir_el_estado_venta()\n",
        "print(mi_departamento.en_venta)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1P5h83Ibjv6",
        "outputId": "41dae055-6d37-4a0a-a017-4adf0f99bd30"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Bonus Track\n",
        "\n",
        "Una clase hija puede tener más de una clase Padre, esto implica que hereda atributos y métodos de amabas clases. Por ejemplo definamos la clase `LocacionGeografica` de manera que sea otra clase padre de Departamento."
      ],
      "metadata": {
        "id": "Umv-yW8wdTgf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LocacionGeografica:\n",
        "    # Define el método de inicialización\n",
        "    def __init__(self, ciudad, salida_al_mar, pais):\n",
        "        self.ciudad = ciudad\n",
        "        self.salida_al_mar = salida_al_mar\n",
        "        self.pais = pais\n"
      ],
      "metadata": {
        "id": "GwKrO_Q8ceXQ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Departamento es la clase hija de la clase Inmueble y de la clase LocacionGeografica\n",
        "class Departamento(Inmueble, LocacionGeografica):\n",
        "    def __init__(self, ventanas, puertas, habitaciones, metros_cuadrados,\n",
        "                 numero_piso, en_venta, ciudad, salida_al_mar, pais):\n",
        "       #Hereda los atributos self, ventanas, puertas, habitaciones, metros_cuadrados de Inmueble\n",
        "       Inmueble.__init__(self, ventanas, puertas, habitaciones, metros_cuadrados)\n",
        "\n",
        "       #Hereda los atributos self, ciudad, salida_al_mar, pais de Locacion Goegrafica\n",
        "       LocacionGeografica.__init__(self, ciudad, salida_al_mar, pais)\n",
        "\n",
        "      #Expande Departamento para añadir el atributo adicional numero de piso\n",
        "       self.numero_piso = numero_piso\n",
        "       self.en_venta = False\n",
        "\n",
        "    #Sobreescribe la función calcular_precio_usd agregando un parametro más en la multiplicación\n",
        "    def calcular_precio_usd(self):\n",
        "        precio_usd = 100*self.metros_cuadrados*self.numero_piso\n",
        "        return precio_usd\n",
        "\n",
        "    def definir_el_estado_venta(self):\n",
        "      self.en_venta= True\n",
        "\n",
        "ventanas = 8\n",
        "puertas = 4\n",
        "habitaciones = 4\n",
        "metros_cuadrados = 100\n",
        "numero_piso = 10\n",
        "en_venta = True\n",
        "ciudad= \"Mar del Plata\"\n",
        "salida_al_mar= True\n",
        "pais ='Argentina'\n",
        "\n",
        "mi_departamento = Departamento(ventanas, puertas, habitaciones, metros_cuadrados,\n",
        "                               numero_piso,en_venta, ciudad, salida_al_mar, pais)\n",
        "\n",
        "print(mi_departamento.salida_al_mar)\n",
        "print(mi_departamento.ciudad)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-LOmKQmd-aH",
        "outputId": "6ac51249-da2e-48b3-dfcc-ba4b788d87e2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "Mar del Plata\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Ejercicio 1**\n",
        "\n",
        "Agreguen un método a la clase `LocacionGeografica`y vuelvan a probar `Departamento`.\n",
        "\n",
        "**Ejercicio 2:**\n",
        "\n",
        "Hacer en un papel o un gráfico en la compu cómo queda el diagrama final de clases y sus conexiones. Hoy en día hay extensiones de los principales editores de texto que lo hacen (por ejemplo vscode lo tiene) por si quieren investigar más."
      ],
      "metadata": {
        "id": "hxcYX1LQgoGH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clases y objetos en librerías de aprendizaje automático\n",
        "\n",
        "Las librerías que venimos usando en la diplomatura ya usaban clases y objetos! Algunos ejemplos:"
      ],
      "metadata": {
        "id": "Q5itSGIq4U-J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.base import BaseEstimator, ClassifierMixin\n",
        "from sklearn.utils.validation import check_X_y, check_array, check_is_fitted\n",
        "\n",
        "\n",
        "df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n",
        "print(\"Tipo de df:\", type(df))\n",
        "print(\"Contenido de df:\\n\", df)"
      ],
      "metadata": {
        "id": "hhsV-Xk5UZ4J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "469a4e5c-cb07-4f30-a8ac-f1572900d56f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tipo de df: <class 'pandas.core.frame.DataFrame'>\n",
            "Contenido de df:\n",
            "    A  B\n",
            "0  1  4\n",
            "1  2  5\n",
            "2  3  6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.DataFrame({'Col1': [4, 5, 6, 'a']})\n"
      ],
      "metadata": {
        "id": "LZT7Dt5dFHOk"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape  # atributo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfQ4z1bjgXtG",
        "outputId": "dc2b6b96-aac6-40e2-a54a-74f1fa26bb4f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns  # otro atributo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fOoIsriKgc-P",
        "outputId": "4407e3df-cbfb-476a-a939-0e1d2b5c1136"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['A', 'B'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjoC1Elzgfoy",
        "outputId": "58627719-d168-4634-a935-711e968b9aff"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=3, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum([1,2,3])  # función \"sin objeto\" (no es método)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1LtEKfpSt9i",
        "outputId": "8d976d27-f87b-4244-fed4-23cda1ae4336"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.max(axis=0)  # método 1 - se llama sobre la instancia df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "nlK3sC_BhqKj",
        "outputId": "1a2a6e85-37b1-4d8b-bdac-238685b1000c"
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      "execution_count": 18,
      "outputs": [
        {
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              "A    3\n",
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      "source": [
        "df.sort_values(by='A')  # método 2"
      ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"A\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          5,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(1)  # no todos es una clase. En este caso, 1 es de tipo primitivo (int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0VG0WsPP6KWD",
        "outputId": "42dbb205-12b7-48ae-e14d-5855b3a196f6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creamos un KNN simple"
      ],
      "metadata": {
        "id": "pV-qxdmzd0Gs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comenzamos inicialmente con init"
      ],
      "metadata": {
        "id": "32hFwT7PeBsy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ModeloKNN:\n",
        "    def __init__(self, k=3):\n",
        "      self.k = k\n",
        "\n",
        "    def fit(self, X, y): # fit es un método\n",
        "      self._X_train = X  # \"entrenamiento\"\n",
        "      self._y_train = y\n",
        "      return self\n",
        "\n",
        "    def predict_knn(self, X):\n",
        "      # WIP\n",
        "      pass"
      ],
      "metadata": {
        "id": "dWLlOf7udtuf"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn1 = ModeloKNN()\n",
        "knn1.fit([[1, 2], [1.5, 1.8]], [0, 0])\n",
        "knn1.k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuLpe6X1gx8m",
        "outputId": "079bd3b2-3a2f-40d2-92ef-d8602abc6841"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn2 = ModeloKNN(k=4)\n",
        "knn2.fit([[2, 2.5], [2.5, 3]], [0, 0])\n",
        "knn2.k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlzlFYwngoKQ",
        "outputId": "12f3c2a7-ba48-4fce-e8d8-3316611c0854"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creados ambos sin utilizar POO, deberíamos crear su variable para cada uno junto con la función de predicción, ej.:"
      ],
      "metadata": {
        "id": "Wg6Ipov43Zpa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn1_k = 3\n",
        "knn2_k = 4\n",
        "\n",
        "# Definiríamos los valores de \"entrenamiento\"\n",
        "# para cada modelo\n",
        "X_knn_1 = np.array([[1, 2], [1.5, 1.8]])\n",
        "y_knn_1 = np.array([0, 0])\n",
        "\n",
        "X_knn_2 = np.array([[2, 2.5], [2.5, 3]])\n",
        "y_knn_2 = np.array([0, 0])\n",
        "\n",
        "def predict_knn(k, X_knn, y_knn):\n",
        "  # WIP\n",
        "  pass"
      ],
      "metadata": {
        "id": "TCsk3tWc574K"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Volvamos a nuestro ejemplo original, y definimos ahora el método predict"
      ],
      "metadata": {
        "id": "3BNTZ7Qe7jhV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ModeloKNN:\n",
        "    def __init__(self, k=3):\n",
        "        self._k = k\n",
        "        self._X_train = None\n",
        "        self._y_train = None\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        self._X_train = X\n",
        "        self._y_train = y\n",
        "        return self\n",
        "\n",
        "    def predict(self, X):\n",
        "        predicciones = []\n",
        "        for x in X:\n",
        "            distancias = [np.sqrt(np.sum((x - x_train)**2)) for x_train in self._X_train]  # distancia euclidiana\n",
        "            k_indices = np.argsort(distancias)[:self._k]  # argsort devuelve los índices ordenados\n",
        "            k_vecinos = [self._y_train[i] for i in k_indices]  # usamos los índices para obtener los valores de y_train\n",
        "            prediccion = max(set(k_vecinos), key=k_vecinos.count)  # obtenemos la moda\n",
        "            predicciones.append(prediccion)  # agregamos la predicción a la lista\n",
        "        return np.array(predicciones)"
      ],
      "metadata": {
        "id": "vvECfwSbb1dS"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear datos de ejemplo\n",
        "X = np.array([\n",
        "    [1, 2],\n",
        "    [1.5, 1.8],\n",
        "    [2, 2.5],\n",
        "    [2.5, 3],\n",
        "    [5, 6],\n",
        "    [5.5, 6.5],\n",
        "    [6, 7],\n",
        "    [7, 8]\n",
        "])\n",
        "y = np.array([0, 0, 0, 0, 1, 1, 1, 1])"
      ],
      "metadata": {
        "id": "vjlukR6hb1D9"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "scatter = plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis', s=100)\n",
        "plt.colorbar(scatter)\n",
        "plt.xlabel('Característica 1')\n",
        "plt.ylabel('Característica 2')\n",
        "plt.title('Visualización de los Datos de Ejemplo para k-NN')\n",
        "for i, (x, y_val) in enumerate(zip(X, y)):\n",
        "    plt.annotate(f'p{i+1}', (x[0], x[1]), xytext=(5, 5), textcoords='offset points')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "F-8DPl4Z2REW",
        "outputId": "4a187bb5-c7b3-469e-9329-9da4d05512af"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Se instancia y entrena el modelo\n",
        "modelo_knn = ModeloKNN(k=3)\n",
        "modelo_knn.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVs-4LnwcYTc",
        "outputId": "a8981065-dc06-4f0f-eb87-86112909eb44"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.ModeloKNN at 0x7d1264cd7d00>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Realizamos predicciones\n",
        "X_nuevo = np.array([[2, 3], [6, 7], [0.5, 7]])\n",
        "predicciones = modelo_knn.predict(X_nuevo)\n",
        "predicciones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAhacAqccWM8",
        "outputId": "97c58748-3fac-406b-f0cd-e4cd88f82c5a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creamos un clasificador KNN que hereda del anterior"
      ],
      "metadata": {
        "id": "-0OnYt6Uc8MU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ModeloKNNMinkowski(ModeloKNN):\n",
        "    def __init__(self, k=3, p=2):  # se agrega p, parámetro de la distancia de Minkowski\n",
        "        super().__init__(k)  # Llamada al constructor de la clase padre\n",
        "        self._p = p  # Parámetro para la distancia de Minkowski\n",
        "\n",
        "    def predict(self, X):  # Sobreescritura del método predict padre\n",
        "        predicciones = []\n",
        "        for x in X:\n",
        "            # Cálculo de la distancia de Minkowski\n",
        "            distancias = [np.sum(np.abs(x - x_train)**self._p)**(1/self._p) for x_train in self._X_train]\n",
        "            k_indices = np.argsort(distancias)[:self._k]\n",
        "            k_vecinos = [self._y_train[i] for i in k_indices]\n",
        "            prediccion = max(set(k_vecinos), key=k_vecinos.count)\n",
        "            predicciones.append(prediccion)\n",
        "        return np.array(predicciones)"
      ],
      "metadata": {
        "id": "mlgxQ1JFbPOs"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Usar los mismos datos de ejemplo\n",
        "modelo_knn_minkowski = ModeloKNNMinkowski(k=3, p=1)\n",
        "modelo_knn_minkowski.fit(X, y)  # se usa el fit definido en ModeloKNN\n",
        "\n",
        "predicciones_minkowski = modelo_knn_minkowski.predict(X_nuevo)\n",
        "predicciones_minkowski"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lK63rC8adIi6",
        "outputId": "60416639-e3fb-4771-8c52-9f68556ed1fc"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vemos gráficamente los resultados"
      ],
      "metadata": {
        "id": "InixnrjmhcDm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_decision_boundary(model, X, y, title):\n",
        "    h = .02  # Tamaño del paso en la malla\n",
        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                         np.arange(y_min, y_max, h))\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu, edgecolors='black')\n",
        "    plt.scatter(X_nuevo[:, 0], X_nuevo[:, 1], c='green', s=200, marker='*', label='Nuevos puntos')\n",
        "    plt.xlabel('Característica 1')\n",
        "    plt.ylabel('Característica 2')\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Visualizar resultados para ModeloKNN\n",
        "plot_decision_boundary(modelo_knn, X, y, 'Área de Decisión - ModeloKNN')\n",
        "\n",
        "# Visualizar resultados para ModeloKNNMinkowski\n",
        "plot_decision_boundary(modelo_knn_minkowski, X, y, 'Área de Decisión - ModeloKNNMinkowski')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LxadJKvpfh8V",
        "outputId": "8b99eb93-0686-45e8-f63e-e5347509445b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Propiedades fundamentales de POO a partir de los modelos"
      ],
      "metadata": {
        "id": "jLtPPFxf7Dwu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "_Abstracción_: usamos ModeloKNN estando abstraídos de sus detalles internos\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "NFZkkuwGCSrL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# imaginemos por ejemplo que lo importamos desde otra librería...\n",
        "# import ModeloKNN\n",
        "\n",
        "modelo = ModeloKNN(k=3)\n",
        "modelo.fit(X, y)\n",
        "prediccion = modelo.predict([[3, 4]])\n",
        "prediccion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3l961TV8BUnf",
        "outputId": "4f94465a-810e-478e-8a16-0f5743f77e93"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "_Encapsulamiento_: los detalles internos se encuentran \"protegidos\""
      ],
      "metadata": {
        "id": "-S3rlx_2CpKE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# el _ de _X_train marca que es de uso dentro\n",
        "# de la clase. El atributo (o método)\n",
        "# no fue pensado para accederse así\n",
        "modelo._X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTQFVXPFCOCN",
        "outputId": "bccc2b57-9d90-4720-d354-61f2ca550090"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1. , 2. ],\n",
              "       [1.5, 1.8],\n",
              "       [2. , 2.5],\n",
              "       [2.5, 3. ],\n",
              "       [5. , 6. ],\n",
              "       [5.5, 6.5],\n",
              "       [6. , 7. ],\n",
              "       [7. , 8. ]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "_Polimorfismo_: tenemos los siguientes modelos, podemos realizar predicciones independientemente de cuál es su clase"
      ],
      "metadata": {
        "id": "Akz9PUzYCW9r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelos = [modelo_knn_minkowski, modelo_knn]"
      ],
      "metadata": {
        "id": "LKmWpkYlAHcd"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imaginemos que los recibimos en una función en otra parte, que hace predicciones a partir de los mismos..."
      ],
      "metadata": {
        "id": "kf9C2ubiCbou"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i, modelo in enumerate(modelos, 1):\n",
        "    modelo.fit(X, y)\n",
        "    prediccion = modelo.predict([[0.3, 7.1]])\n",
        "    print(f\"Predicción del Modelo {i}\\\n",
        "            para [0.3, 7.1]: {prediccion}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Z_UdhVV-4Qc",
        "outputId": "b51f9ac7-bbb4-45cc-f55e-64006f7acfd5"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicción del Modelo 1            para [0.3, 7.1]: [1]\n",
            "Predicción del Modelo 2            para [0.3, 7.1]: [0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1u9_yCY9_upz"
      },
      "execution_count": 36,
      "outputs": []
    }
  ]
}