Ejercicios_05_ModelosLineales.ipynb

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      "source": [
        "# Por si alguien corre en python2\n",
        "from __future__ import division\n",
        "\n",
        "# Preparamos todo para correr\n",
        "import numpy as np\n",
        "from math import *\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "from scipy.stats import norm, binom, gamma, poisson, multivariate_normal\n",
        "from sklearn import linear_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Ejercicio 1: Fitteando una recta ruidosa de manera Bayesiana.\n",
        "\n",
        "Como primer ejemplo de regresión lineal, tratemos de fittear los parametros $a_{0}$, $a_{1}$ de una recta $f(x,\\vec{a}) = a_0 + a_{1}x$ donde $x$ está en el intervalo $[-1,1)$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xmazAIfPtWne"
      },
      "source": [
        "* **A)** Genere un set artificial de datos usando, usando como valores $a_{1} = 0.5$, $a_{0}= -0.3$. Añada a las mediciones ruido gaussiano  $\\epsilon \\sim \\mathcal{N}(\\mu=0, \\sigma = 0.2)$. \n",
        "Al terminar debería tener un conjunto de $N= 100$  pares $(x_n,t_n)$, con \n",
        "$t_n = f(x_n,\\vec{a}) + \\epsilon$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ky5iN8XrtIZW",
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YWYDby8puqkv"
      },
      "source": [
        "* **B)** Como modelo usaremos una regresión lineal de la forma $y(x) = \\omega_0 + \\omega_1 x$. ¿Cuál base de funciones $\\phi_j$ estamos usando? (recuerde que por convención $\\phi_0(x)=1$). \n",
        "Escriba para estos la matriz de diseño.\n",
        "$$\\Phi = \\begin{pmatrix}\n",
        "\\phi_0(x_1) & \\phi_1(x_1)\\\\\n",
        "\\phi_0(x_2) & \\phi_1(x_2)\\\\\n",
        "\\vdots & \\vdots)\\\\\n",
        "\\phi_0(x_N) & \\phi_1(x_N)\\\\\n",
        "\\end{pmatrix}$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Xhcx-wbnusXa",
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "markdown",
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      "source": [
        "* **C)** Estamos interesados en encontrar los valores de $\\omega_0$ y $\\omega_1$ de nuestro modelo más probables, dado los datos que tenemos. Estos están dados por el máximo de nuestra distribución posterior. Si usamos priors uniformes en $\\omega_0$ y $\\omega_1$ para caracterizar nuestro desconocimiento, el máximo del posterior coincide con el de la verosimilitud y es lo que llamamos el *estimador de máxima verosimilitud*. De acuerdo a lo visto en la teórica, dicho valor esta dado por \n",
        "$$\\begin{pmatrix}\n",
        "\\omega_0^\\text{ML}\\\\\n",
        "\\omega_1^\\text{ML}\\\\\n",
        "\\end{pmatrix} \n",
        "= \\left(\\Phi^T \\Phi\\right)^{-1}\\Phi^T \\,\\vec{t}\n",
        "$$\n",
        "donde recordemos que $\\vec{t} = \\begin{pmatrix}t_1\\\\ \\vdots \\\\ t_N\\end{pmatrix}$ es el vector de los valores *target* medidos.\n",
        "\n",
        "Nota: Para un modelo lineal con error gaussiano, este estimador de maxima verosimilitud coincide con lo que se conoce como la solución de *cuadrados mínimos*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0OWcChEAuuU9",
        "colab": {}
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
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      "source": [
        "* **D)** Si en vez de utilizar un prior uniforme, utilizamos un prior gaussiano de la forma $p(\\vec{\\omega}) = \\mathcal{N} (\\vec{0},\\alpha^{-1}{\\bf 1})$ (es decir que $p(\\vec{\\omega}) = p(\\omega_0) \\times p(\\omega_1)$ con cada $p(\\omega_i) = \\mathcal{N} (\\mu=0,\\sigma = \\alpha)$ ). Calcule la verosimilitud y el posterior (prior*posterior normalizado) al usar solo 1 punto, 2 puntos, 3 puntos y todo el conjunto de 100 puntos.\n",
        "\n",
        "\n",
        "Si se le complica hacerlo en forma numerica, puede utilizar la formula analitica dle posterior: Como este es un prior conjugado a la gaussiana en la teórica (o en su defecto, en el Bishop ecuacion 3.53), vimos que nuestro posterior es una gaussiana con valor medio\n",
        "$$\n",
        "\\vec{m}_N = \\beta {\\bf S}_N \\Phi^T \\vec{t}\\\\\n",
        "{\\bf S}_N^{-1} = \\alpha {\\bf 1} + \\beta \\Phi^T \\Phi\n",
        "$$\n",
        "donde $\\beta$ es el parámetro de precisión del ruido gaussiano, que se supone conocido. En nuestro caso sería $\\beta = (1/\\sigma)^2 = (1/0.2)^2 = 25$. Para seleccionar la cantidad de puntos a considerar, puede usar slicing en $\\vec{t}$ ( ``t[:N_puntos]``) y en $\\Phi$ (``Phi[:N_puntos,:]``)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
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        "colab": {}
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6PzLAyAa_HMO"
      },
      "source": [
        "# Ejercicio 1 bis: Scikit-learn\n",
        "\n",
        "Scikit-learn es una librería de machine learning con gran soporte para una multitud de algoritmos de análisis de datos, que nos permite acceder a ellos de una forma muy similar, obviando las diferencias técnicas entre sus implementaciones. En la práctica, nadie escribe sus propias funciones para algoritmos (a menos que sea un algoritmo novedoso que estás diseñando), sino que utilizamos las implementaciones provistas por este u otro paquete."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XufupgU5_t6T"
      },
      "source": [
        "* **A)** Scikit-learn provee una clase LinearRegressor la cual permite obtener las soluciones de cuadrados mínimos en problemas lineales. Importe el paquete, y examine las distintas funciones leyendo la documentación provista en el paquete. Si no es suficiente, [visite la documentación on-line](https://scikit-learn.org/), para familiarizarse con como se utiliza."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1dD8ficdARWP",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg = LinearRegression()\n",
        "#descomente cada linea para ver la documentación.\n",
        "#LinearRegression?\n",
        "#reg.fit?\n",
        "#reg.predict?\n",
        "#reg.score?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NPnYV5bXAc2k"
      },
      "source": [
        "* **B)** Utilizando los datos del ejercicio anterior, repita el inciso **C)** utilizando scikit-learn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xruB3zXLAcVx",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iQF-aGNL4PoM"
      },
      "source": [
        "# Ejercicio 2: Algoritmo de Cuadrados Mínimos para encontrar la mejor base de funciones\n",
        "\n",
        "El algoritmo de cuadrados mínimos consiste en hallar los parámetros que minimicen la distancia cuadrática entre los datos y mi ajuste $$ E_{D}(\\vec{w})=\\frac{1}{2}\\left(t-\\vec{w}^{T}\\vec{\\phi}(\\vec{x})\\right)^{2}.$$ Este puede verse como el estimador de máxima verosimilitud cuando modelamos los errores como gaussianos en un problema lineal.\n",
        "\n",
        " Considere el siguiente set de datos ``X``y ``T`` como dado:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nTmm_AQT4Sj2",
        "outputId": "448a7ca5-e2be-47ee-c9c0-c50710ba87dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
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      },
      "source": [
        "X=-1.0+2.0*np.random.rand(100)\n",
        "T=np.asarray(list(map(lambda x: 1.0*np.sin(6*x)+0.0*norm.rvs(loc=0.0,scale=0.1),X)))\n",
        "T.shape\n",
        "plt.scatter(X,T)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fedbc6f3f98>"
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          "execution_count": 4
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ZjrE0AiWCBqVBPNWnYyyNQlVDDUqDeKpPx1gahRJBg9IgnurTMZZGoUTQoKIG62gQT3LO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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "51HYcOYa572S"
      },
      "source": [
        "* **A)** Considere la base de funciones los polinomios $\\phi_j(x) = x^j$, con $j=1,\\dots,M$. Calcule la matriz de diseño."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ADlzSf7Z5eei",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZH04_o1a7LQS"
      },
      "source": [
        "* **B)** Calcule la solución de cuadrados mínimos, utilizando la expresión dada en el inciso **C)** del **Ejercicio 1**, (la ecuación 3.15 del Bishop; ecuaciones normales). Grafíquela encima de los datos. Estudie como cambia para distintos tamaños de la base, i.e. distintos valores de $M$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "22815mUD7dfj",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3Tib1NLO7eEJ"
      },
      "source": [
        "* **C)** Una forma de aproximar la solución cerrada de forma iterativa consiste en aplicar el algoritmo de Descenso por Gradiente para minimizar la función $E_D(\\vec{\\omega})$:\n",
        "$$\n",
        "\\vec{w}^{\\tau+1}=\\vec{w}^{\\tau}-\\eta\\nabla E_{n}(\\vec{w}^{\\tau})\\\\\n",
        "=\\vec{w}^{\\tau}-\\eta(t_{n}-\\vec{w}^{\\tau T}\\vec{\\phi}(\\vec{x}_n))\\vec{\\phi}(\\vec{x}_{n}).\n",
        "$$\n",
        "Donde partimos de un valor $\\vec{\\omega}^0$ aleatorio que nos permite calcular de forma iterativoa los $\\vec{\\omega}^\\tau$ subsiguientes en función del parametro $\\eta$ (también llamado *learning rate*). Examine como se va acercando este algoritmo a la solución cerrada calculada en el inciso anterior, para un valor de $\\eta=0.2$. Estudie como varia al cambiar $\\eta$ y de $\\omega_0$.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kpmXmr1p8chh",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "StnkcHgg9Umf"
      },
      "source": [
        "* **D)** Se puede ver que incluir un prior normal centrado en cero para los parámetros, equivale a agregar a la función de cuadrados mínimos un término regularizador\n",
        "$$E_{D}(\\vec{w})=\\frac{1}{2}\\sum_{i=1}^{N}(t_{i}-\\vec{w}^{T}\\vec{\\phi}(\\vec{x_{i}}))^{2}+\\frac{\\kappa}{2}\\vec{w}^{T}\\vec{w}.$$\n",
        "Esto se traduce, en el algoritmo recursivo, en la siguiente expresión\n",
        "$$\n",
        "\\vec{w}^{\\tau+1}=\\vec{w}^{\\tau}-\\eta\\left[(t_{n}-\\vec{w}^{\\tau T}\\vec{\\phi}(\\vec{x}_n))\\vec{\\phi}(\\vec{x}_{n}) + \\kappa \\vec{\\omega}^{\\tau}\\right].\n",
        "$$\n",
        "Modifique el código del inciso anterior, y para un valor de $\\kappa$ de su elección, estudie que sucede (puede comparar con el caso $\\kappa=0$ que coincide con el inciso anterior)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CNC_K7wI9k_p",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UeXfUP-f-cfu"
      },
      "source": [
        "* **E)** Separe el set de datos en training y validation en una proporción de 0.8/0.2. En base a lo visto en la teoría, ¿observa overfitting? ¿a que se debe? ¿cómo lo reduciría? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "G27ycGcx-vC0",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rvRYHGmgA_5g"
      },
      "source": [
        "# Ejercicio 2 bis: Scikit-learn\n",
        "\n",
        "Utilizar una base de funciones $\\phi_j(x)$ es lo que en la jerga de data science se conoce como *feature extraction*. Transformamos nuestros datos $x$ en features $\\tilde x = \\phi_j(x)$, los cuales usamos para alimentar nuestros modelos (por ejemplo, el Regresor Lineal de los ejercicios anteriores. Este tipo de transformaciones es parte de lo que se conoce como *pre-procesado* de los datos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ULJIAyVjCRca"
      },
      "source": [
        "* **A)** Importe de ``sklearn.preprocessing`` la clase ``PolynomialFeatures``. Examine su documentación para ver que es lo que hace."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g4j5wJkkBArO",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "osaITEz4CpPV"
      },
      "source": [
        "* **B)** Utilizando ``PolynomialFeatures`` y ``LinearRegressor`` repita el inciso **C** del ejercicio anterior. \n",
        "\n",
        "Si le interesa aprender una forma mas elegante de tratar con preprocesado (que podría ser util si se realizan mas transformaciones previas), examine el importe la clase ``Pipeline`` de ``sklearn.pipeline``, e intente usarla con ayuda de [los ejemplos de la documentación](https://scikit-learn.org/stable/modules/linear_model.html#polynomial-regression-extending-linear-models-with-basis-functions)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Jnc8jwYUCs0k",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UlCJucFnEX08"
      },
      "source": [
        "* **C)** Examine la instancia de ``SGDRegressor``, y usando el argumento ``loss=\"squared_loss\"`` utilicelo para resolver el inciso **D** anterior de forma iterativa (será util el argumento ``learning_rate=constant`` y ``eta0=`` para comparar con la realizada en el ejercicio anterior)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zjtlsg_EE2i1",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import SGDRegressor\n",
        "SGDRegressor?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cbwR2wZn2pG1"
      },
      "source": [
        "# Ejercicio 3: Cross-validation para comparación de modelos\n",
        "\n",
        "En este ejercicio utilizaremos el set de datos de precios inmobiliarios en el estado de Boston, que viene con scikit-learn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "i74DLP2P2pG1",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline\n",
        "\n",
        "import pandas as pd  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EzJXw3vW2pG4"
      },
      "source": [
        "En la siguiente celda cargamos el dataset, y con el creamos un DataFrame de Pandas. Scikit-learn nos entrega un diccionario con los datos, el nombre de cada feature, el target, y una descripción general del dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-_aGJEVq2pG5",
        "outputId": "bfb639b0-7476-4c2d-965e-38b79884b97f",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "\n",
        "boston_dataset = load_boston()\n",
        "boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
        "#añadimos el target como una columna llamada 'MEDV'\n",
        "boston['MEDV'] = boston_dataset.target\n",
        "boston.head()"
      ],
      "execution_count": 0,
      "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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  MEDV  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     17.8  392.83   4.03  34.7  \n",
              "3     18.7  394.63   2.94  33.4  \n",
              "4     18.7  396.90   5.33  36.2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "l4x6b_IM2pHA"
      },
      "source": [
        "Puede leer la descripción de cada feature ejecutando la siguiente celda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XDKeI3uG2pHB",
        "colab": {}
      },
      "source": [
        "print(boston_dataset.DESCR[296:1226])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WbM3KMqr2pHD"
      },
      "source": [
        "El objetivo de este ejercicio es predecir el viviendas de Boston (\"MEDV\") a partir del resto de los features.\n",
        "\n",
        "Para simplificar la tarea, solo consedere los dos features mas correlacionados con el target \"MEDV\". La siguiente lista, y los gráficos de correlación (ploteados por el muy util paquete ``seaborn``), pueden ayudarlo a elegir."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mjB0TP4U2pHE",
        "outputId": "0a8b0124-358f-4a29-80f9-38af191b02c0",
        "colab": {}
      },
      "source": [
        "from seaborn import pairplot\n",
        "pairplot(boston, x_vars=boston.columns,y_vars='MEDV')\n",
        "abs(boston.corr()['MEDV']).sort_values(ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MEDV       1.000000\n",
              "LSTAT      0.737663\n",
              "RM         0.695360\n",
              "PTRATIO    0.507787\n",
              "INDUS      0.483725\n",
              "TAX        0.468536\n",
              "NOX        0.427321\n",
              "CRIM       0.388305\n",
              "RAD        0.381626\n",
              "AGE        0.376955\n",
              "ZN         0.360445\n",
              "B          0.333461\n",
              "DIS        0.249929\n",
              "CHAS       0.175260\n",
              "Name: MEDV, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2520x180 with 14 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PuDVVORa2pHi"
      },
      "source": [
        "**A)**\n",
        "Divida sus datos en Training /Test, usando en una proporcion de 0.8/0.2. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eZSaBJiW2pHl",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "97GMY6Pq2pHq"
      },
      "source": [
        "**B)** Utilice validación cruzada y K-folding para comparar el desempeño de diferentes modelos lineales utilizando solamente los datos de Training. Utilice la métrica de desempeño ``scoring='neg_root_mean_squared_error'``. Los modelos a comparar son:\n",
        "* Regresión lineal unidimensional utilizando cada uno de los features escogidos.\n",
        "* Regresión lineal bidimensional utilizando ambos features.\n",
        "* Regresión polinomial cuadrática unidimensional utilizando cada uno de los features escogidos.\n",
        "* Regresión polinomial cuadrática  bidimensional utilizando ambos features.\n",
        "En base a estos resultados, elija un modelo. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mDx0xsTz2pHq",
        "colab": {}
      },
      "source": [
        "import sklearn.preprocessing as pp\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-oDcJVhd2pHt"
      },
      "source": [
        "**C)** Considere la regresión polinomial  cuadrática de los ultimos dos casos. Añada un término de regularización LASSO y RIDGE. Utilice validación cruzada para medir su desempeño, ¿cómo se compara a los casos anteriores? ¿Puede elegir los valores óptimos para la regularización?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "S7mm6PjO2pHt",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import Ridge, Lasso, RidgeCV, LassoCV\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "azENXu732pHw"
      },
      "source": [
        "**D)**  (*) Considere una Regresión polinomial de grado ``M`` con regularización (LASSO o RIDGE). Utilice validación cruzada para obtener la mejor combinación de parámetros ``M`` y ``alpha`` (el regulador)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zdPp0vDV2pHw",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Jo6WBNpc3bXR"
      },
      "source": [
        "**E)**  (*) Opcional: Explore el dataset. Pruebe incluír mas features,  y explore las diversas funcionalidades vistas en clase: Estratificación, distintas funciones de base, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XmfibYjv3tR5",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}