28_Clasificacion_Regresion_Logistica_Scikit_Iris.ipynb

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    "# 28 - Clasificación:  Regresión Logística con Scikit-Learn\n",
    "\n",
    "\n",
    "* En este Notebook vamos a resolver un problema de ***Clasificación Binaria con las Regresión Logística usando la librería de Scikit-Learn***.\n",
    "\n",
    "\n",
    "* Para resolver el problema de Clasificación vamos a utilizar la clase **\"LogisticRegression()\"**, cuya documentación esta en el siguiente enlace:<br>\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "\n",
    "\n",
    "* El ejemplo con el que vamos a trabajar, consiste en ***clasificar dos tipos de flores, conociendo el tamaños del sépalo*** de la flor.\n",
    "\n",
    "\n",
    "* El objetivo es encontrar los parámetros $\\beta_0, \\beta_1, \\beta_2$ que mejor se ajustan a esos datos, para que podamos discernir una clase u otra a partir de la siguiente función de decisión:\n",
    "\n",
    "<span style=\"font-size:16px\">$$Z = \\beta_0 + \\beta_1 \\cdot SepalLenght + \\beta_2 \\cdot SepalWith$$</span>\n",
    "    \n",
    "    \n",
    "* Aplicando el resultado de la función de decisión a una logística podremos saber la probabilidad de pertenencia de cada flor a una clase u otra de la siguiente manera:\n",
    "<span></span><br><br>\n",
    "<span style=\"font-size:16px\">$$P(Y=1|X_1,X_2) = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1 \\cdot X_1 + \\beta_2 \\cdot X_2)}}$$</span>\n",
    "<span></span><br><br>\n",
    "<span style=\"font-size:16px\">$$P(Y=0|X_1,X_2) = 1 - \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1 \\cdot X_1 + \\beta_2 \\cdot X_2)}}$$</span>\n",
    "\n",
    "\n",
    "* Para resolver este problema vamos a realizar los siguientes pasos:\n",
    "<span></span><br>\n",
    "    1. [Carga de datos](#M1)\n",
    "<span></span><br>\n",
    "    2. [Cambio de estructura de datos a numpy y transformación de las etiquetas a números](#M2)\n",
    "<span></span><br>\n",
    "    3. [Visualización de los datos](#M3)\n",
    "<span></span><br>\n",
    "    4. [Creacción del modelo (hipótesis) y ajuste](#M4)\n",
    "<span></span><br>\n",
    "    5. [Obtención del modelo (hipótesis)](#M5)\n",
    "<span></span><br>\n",
    "    6. [Visualización de los resultados](#M6)\n",
    "\n",
    "\n",
    "* En primer lugar vamos a importar las librería necesarias:\n",
    "\n",
    "<hr>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## <a name=\"M1\">1. Carga de datos</a>\n",
    "\n",
    "\n",
    "* El primer paso que vamos a realizar es el de cargar los datos que los tenemos en el siguiente fichero:\n",
    "    - ***DataSet***: \"./data/iris/iris_2_categorias.csv\"\n",
    "\n",
    "\n",
    "* Para ello ***leeremos el csv con pandas*** (pasandolo a un dataframe), teniendo las siguientes columnas que nos interesan para el problema a resolver\n",
    "    - ***class***: Clase de la flor (Iris-versicolor, Iris-setosa)\n",
    "    - ***sepal_length***: El \"largo\" del Sepalo\n",
    "    - ***sepal_width***: El \"ancho\" del Sepalo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width            class\n",
       "54           6.5          2.8           4.6          1.5  Iris-versicolor\n",
       "38           4.4          3.0           1.3          0.2      Iris-setosa\n",
       "20           5.4          3.4           1.7          0.2      Iris-setosa\n",
       "30           4.8          3.1           1.6          0.2      Iris-setosa\n",
       "67           5.8          2.7           4.1          1.0  Iris-versicolor"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/iris/iris_2_categorias.csv\")\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## <a name=\"M2\">2. Cambio de estructura de datos a numpy y transformación de las etiquetas a números</a>\n",
    "\n",
    "\n",
    "* Scikit necesita como entrada para el ajuste (o entrenamiento) del modelo los datos del Dataset de la siguiente forma:\n",
    "<span></span><br><br>\n",
    "    - ***Variables de entrada X***: ***Array de Arrays de numpy*** en el que cada array tiene las variables de entrada de un elemento.\n",
    "<span></span><br><br>\n",
    "    - ***Variable de salida y*** (solo para aprendizaje supervisado): ***Array de numpy*** en el que cada posición del array contiene la salida o el valor esperado del elemento del Dataset.\n",
    "\n",
    "\n",
    "* Por tanto, vamos a pasar los datos las flores a una nueva estructura de datos para que scikit-learn puedan trabajar con los datos:\n",
    "<span></span><br><br>\n",
    "    - Las ***columnas sepal_length y sepal_width*** representan las variables de entrada y las vamos a pasar a un array de array (***numpy array***)\n",
    "<span></span><br><br>\n",
    "    - La ***columna class*** que representa la clase (o variable de salida) y la vamos a pasar a un ***numpy array***\n",
    "    \n",
    "    \n",
    "* Por otro lado tenemos que transformar la clase de la flor a números, por lo que utilizaremos la clase ***LabelEncoder()*** para transformar las etiquetas de las clases a números:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "X = df[['sepal_length', 'sepal_width']].values   # Obtengo el alto y ancho del sepalo\n",
    "y_labels = df['class'].values                    # Obtengo las etiquetas\n",
    "\n",
    "\n",
    "le = preprocessing.LabelEncoder()     # Instancio un objeto de la clase LabelEncoder()\n",
    "y = le.fit_transform(y_labels)        # Transformo las etiquetas a números"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## <a name=\"M3\">3. Visualización de los datos</a>\n",
    "\n",
    "\n",
    "* A continuación vamos a ver la relación que hay entre las dos variables de los dos tipos de flores, en la que podemos observar como podemos trazar una linea (Decision Boundary) entre las dos clases para separarlas:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23632d68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=\"sepal_length\", y=\"sepal_width\", hue=\"class\", data = df, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## <a name=\"M4\">4. Creacción del modelo (hipótesis) y ajuste</a>\n",
    "\n",
    "\n",
    "* A continuación pasamos a crear un objeto de la clase \"***LogisticRegression()***\" para la resolución del problema de clasificación.\n",
    "\n",
    "\n",
    "* Una vez instanciado, procedemos a ajustar (entrenar) el modelo (o hipótesis) llamando al método ***fit(X,y)*** que recibe como parámetros el conjunto de datos de las variables de entrada 'X' y la variable de salida 'Y'.\n",
    "\n",
    "\n",
    "* Una vez ajustado (o entrenado) el modelo (o hipótesis), tendremos ajustados los parámetros ***$\\beta_n$*** de la siguiente manera:\n",
    "\n",
    "<span style=\"font-size:16px\">$$Z = \\beta_0 + \\beta_1 \\cdot SepalLenght + \\beta_2 \\cdot SepalWith$$</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=50, multi_class='ovr', n_jobs=None,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos un objeto de la clase LogisticRegression pasandole una serie de parámetros\n",
    "logreg = LogisticRegression(solver='liblinear', multi_class='ovr', max_iter=50)\n",
    "\n",
    "# Entrenamos el modelo siendo la 'X' el tamaño del sepalo e 'y' el tipo de flor (0 = Iris-setosa, 1 = Iris-versicolor)\n",
    "logreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## <a name=\"M5\">5. Obtención del modelo (hipótesis)</a>\n",
    "\n",
    "\n",
    "* El objeto de la clase ***LogisticRegression*** nos proporciona los resultados de los parámetros $\\beta_n$ por medio de los atributos de la clase:\n",
    "<span></span><br><br>\n",
    "    - ***coef_***: Nos devuelve en una lista los parámetros del $\\beta_1$ al $\\beta_n$.\n",
    "<span></span><br><br>\n",
    "    - ***intercept_***: Nos devuelve el parámetro ***$\\beta_0$***\n",
    "    \n",
    "\n",
    "* Obteniendo estos valores podemos obtener el modelo generado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z = -0.60 + 2.22 · sepal_length + -3.69 · sepal_width\n"
     ]
    }
   ],
   "source": [
    "b0 = logreg.intercept_[0]\n",
    "b1 = logreg.coef_[0,0]\n",
    "b2 = logreg.coef_[0,1]\n",
    "\n",
    "print(\"Z = {b0:0.2f} + {b1:0.2f} · sepal_length + {b2:0.2f} · sepal_width\".format(b0=b0, b1=b1, b2=b2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## <a name=\"M6\">6. Visualización de los resultados</a>\n",
    "\n",
    "\n",
    "* A continuación pasamos a representar en un espacio de dos dimensiones cómo clasificaría nuestro modelo en ese espacio 2D y cómo clasifica los puntos del Dataset con el que ha entrenado:\n",
    "\n",
    "\n",
    "* En primer lugar vamos a definir una malla de puntos (\"meshgrid\") que se utilizará para pintar las regiones de cada clase. El objetivo de construir esta malla es la de predecir cada punto de la malla como si fuesen los datos de entrada de una flor con esos valores de alto y ancho.\n",
    "\n",
    "\n",
    "* Para ello miramos los valores máximos y mínimos de los tamaños de la flor para acotar la región a pintar y posteriormente generamos el conjunto de puntos a predecir, llamando a la función ***\"predict(X)\"***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "h = .01\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "# Predicción de cada uno de los puntos de la malla\n",
    "Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pasamos a pintar las regiones de cada clase así como los datos del dataset.\n",
    "\n",
    "\n",
    "* De manera visual, podemos ver como el modelo generado es capaz de clasificar correctamente todas las flores del Dataset excepto 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(8, 6))\n",
    "\n",
    "# Dibujamos la malla con los diferentes colores\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel1)\n",
    "\n",
    "\n",
    "# Dibujamos los puntos (flores) del dataset\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Pastel1)\n",
    "\n",
    "plt.title('Clasificación de las Flores')\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "*Este Notebook ha sido desarrollado por **Ricardo Moya García** y registrado en Safe Creative como ***Atribución-NoComercial-CompartirIgual***.*\n",
    "\n",
    "<img src=\"./imgs/CC_BY-NC-SA.png\" alt=\"CC BY-NC\">"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}