3. Aprendizaje Supervisado - Regresion Logistica.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Importamos nuestra librerías básicas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import itertools\n",
    "import random\n",
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Este paso es para que los resultados en vuestros notebooks sean iguales a lo de este\n",
    "np.random.seed(42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Aprendizaje supervisado - Regresión Logistica\n",
    "\n",
    "En este notebook vamos implementar la clasificación de digitos de la serie MNIST\n",
    "En la mayoría de nuestros ejemplos utilizaremos la librería de Machine Learning \n",
    "Scikit-Learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Como funciona la regresion logistica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kF0vaX9Kdko4pQe2/K+klkr4jabTBfg9IWl10vcupv8Tn/m8lXZCWL6j1c5M+\n+2XRtTZ7HiX9maTPpOUzJX2p6Lr3ofZzJF1cdK01an+tpFdKuqvO56dKukaSJR0n6ftF17yP9Z8o\n6Z+LrrNGXYdJemVaPkjSj2r8zCz73Hd1SzEiro+IXWn1VklDNXZbJ2l7RNwfEU9JukLS+rxqrCci\n7o6Ie4uuY7marL+U515ZDZem5UslvbXAWprRzHms/m/6qqSTbDvHGusp68/AkiLiZkmPNthlvaQv\nROZWSc+3fVg+1S2tifpLKSIeiojb0/IvJN0t6YhFuy373Hd1KC5yrrLfHBY7QtKDVesz2vsEl1lI\nut72Ftsbiy5mH5X13B8SEQ9J2V9ASS+ss9+Btidt32q7yOBs5jw+s0/6RXFO0gtyqa6xZn8GzkiX\nwb5qe00+pa1YWX++98Xxtu+0fY3tlxZdzGLpNsArJH1/0UfLPvf7taKwItm+UdKhNT66MCKuSvtc\nKGmXpMtqfUWNbbl0yW2m9iacEBE7bL9Q0g2270m/AbZdC+ov5bnfh68ZTuf+xZK+ZXtrRNzXmgr3\nSTPnsbBzvYRm6vqGpMsj4knb71LW4n192ytbubKe82bdrmxotF/aPlXS1yUdXXBNz7D9XElfk/S+\niHhs8cc1/khT577jQzEiTm70ue2zJb1Z0kmRLjYvMiOp+jfPIUk7WldhfUvV3uR37EjvD9u+Utnl\nqFxCsQX1l/Lc2/6p7cMi4qF0yeXhOt8xf+7vt/0dZb+xFhGKzZzH+X1mbO8nqV/luHS2ZO0R8UjV\n6j8o6x/QCQr7+W6F6qCJiKttf9r26ogofFxU289SFoiXRUSlxi7LPvddffnU9imS3i/ptIh4vM5u\nt0k62vaRtvdX1gmh0J6EzbL9HNsHzS8r61hUsydZSZX13G+WdHZaPlvSXq1e2wO2D0jLqyWdIOmH\nuVW4p2bOY/V/09skfavOL4l5W7L2RfeCTlN2D6kTbJb0J6kn5HGS5uYvy3cC24fO33e2vU5ZXjzS\n+E+1X6rpc5Lujoj/VWe35Z/7onsStfMlabuy68p3pNd877vDJV1dtd+pynow3afs0l8Zaj9d2W87\nT0r6qaTrFteurMfenem1rSy1N1t/ic/9CyTdJOnH6f3gtH1U0j+m5d+XtDWd+62SNhRc817nUdKH\nlf1CKEkHSvpK+jvxA0kvLvo870Ptf5N+vu+U9G1Jv1N0zamuyyU9JOk36Wd9g6R3SXpX+tySPpX+\nu7aqQS/yktZ/ftV5v1XS7xddc6rr3yu7FDpV9W/7qa0694xoAwBA0tWXTwEA2BeEIgAACaEIAEBC\nKAIAkBCKAAAkhCLQAlUzZtyPlawdAAAC4klEQVRl+yu2f6vOfle7zmwtefDCrCp32r7edq1RfZbz\nvefYvrgV3wUUiVAEWuOJiHh5RBwr6Sllz0w9Iz1E3BcRp0bEz1t54DRCzb54XUS8TNKkpA/U+L5V\nLSkM6ECEItB635N0lO21ac63TysbR3JNaqmtTp/dY/sfU+vyMtsn277F2TyO66RnRi26xPZttv+f\n7fVp+zmpRfoNZQPCP9f2TbZvTy3BZmaauFnSUen7fmn7w7a/r2wQ6AfSSD2yPZqGsatbT7LG9rXO\n5kb80PxG2193NmD9NnfeoPXoMR0/9ilQJqnV9iZJ16ZNL5H0pxHxZ+nz6t2PkvR2SRuVDXf2DmWj\ndZymrAX3VmUDlH8rIs5Nl11/kAYzl6TjJY1ExKPpuKdHxGMpzG61vTkaj87xZmWjfUjSc5TNq/fB\nGnVWa1TPOknHSnpc0m22vxkRk5LOTTU+O23/Wuw5nilQGoQi0BrPtn1HWv6esrEZD5f0k8jmc6vl\nXyNiqyTZ3ibppogI21slrU37vFHSabbH0/qBkobT8g0RMT+otyX9te3XStqtbJqcQyT9W43jftv2\n08qGyfrLtO1pZQMsL2Wpeh5J/z0VZQE/Kek9tk9P+6xRNtMCoYhSIhSB1ngiIl5evSG1tn7V4M88\nWbW8u2p9txb+blrSGbFowmbbr1n03e+UNCjpVRHxG9sPKAusWl4Xe8908OuIeLpqfZcWbq9Uf0+j\neha3SsP2iZJOlnR8RDyeLsPWqwsoHPcUgXK7TtK7q2YreEWd/folPZwC8XWSXrTC4z4g6VVp+Ywm\n63mD7YPTZdK3Srol1TWbAvF3JB23wrqAtiIUgXL7K0nPkjRl+660XstlkkZtTyprNd6zwuP+d0kf\nt/09ZZdWm6nnXyR9UdmsBV9L9xOvlbSf7am0b71LyUApMEsGAAAJLUUAABJCEQCAhFAEACAhFAEA\nSAhFAAASQhEAgIRQBAAgIRQBAEj+PzNwInbAy7eCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21c9414de80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para empezar creamos un dataset ejemplo con sklearn\n",
    "data = make_classification(n_samples=30, n_features=2, n_informative=2, n_redundant=0,\n",
    "                           weights=[.6, .4], random_state=42)\n",
    "# Separo entre *features* y el objetivo\n",
    "scale = StandardScaler().fit(data[0])\n",
    "features = scale.transform(data[0])\n",
    "target = data[1]\n",
    "# Dibujo las muestras\n",
    "nok = features[target==0]\n",
    "ok = features[target==1]\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel(\"Primera Prueba\")\n",
    "plt.ylabel(\"Segunda Prueba\")\n",
    "plt.scatter(nok[:,0], nok[:,1], color=\"red\", alpha=0.5)\n",
    "plt.scatter(ok[:,0], ok[:,1], color=\"blue\", alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Aplicamos la regresión logísitica para separar las muestras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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19UmTkzO3TU5m2wGgQxGKnWpwMOtHnJiQpqZ2Lw8Oll0ZAJSGUOxU/f3S0FDW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\n/weQaYQlA1RASAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21c94165710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aplicamos la regresión logistica\n",
    "log_reg = LogisticRegression().fit(features, target)\n",
    "# Volvemos a dibujar las muestras\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.xlabel(\"Primera Prueba\")\n",
    "plt.ylabel(\"Segunda Prueba\")\n",
    "plt.scatter(nok[:,0], nok[:,1], color=\"red\", alpha=0.5)\n",
    "plt.scatter(ok[:,0], ok[:,1], color=\"blue\", alpha=0.5)\n",
    "lin = np.array([-2,2])\n",
    "w0 = log_reg.intercept_\n",
    "w1,w2 = log_reg.coef_[0]\n",
    "plt.plot(lin,(-w0-w1*lin)/w2,\"k-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Ahora vamos aplicar la regresión logística a un dataset más complicado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Aqui importamos los datos que vamos a clasificar\n",
    "\n",
    "# Scikit-Learn ya incluye algunos datasets de ejemplo como el MNIST\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "\n",
    "# El dataset contiene 70.000 ejemplos de digitos escritos a mano\n",
    "# en blanco y negro. Cada digito esta representada por una imagen de 28x28 pixels.\n",
    "# Además el dataset incluye el \"target\" i.e. el numero asociado con cada imagen.\n",
    "\n",
    "# Otros datasets serían más rápidos de ejecutar, pero la ventaja de este es que,\n",
    "# debido a su complejidad, cuando empecemos a utilizar modelos más complejos (como \n",
    "# redes neuronales), se empezarán a ver las ventajas de estos modelos más complejos\n",
    "\n",
    "# Aqui cargamos nuestros ejemplos en X, el target en y. Nuestro objetivo con\n",
    "# Machine Learning es aprender la función f(X) que genera y.\n",
    "X, y = mnist[\"data\"], mnist[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Separación del set de pruebas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Nuestro objetivo es que la función aprendida funcione no solamente con\n",
    "# el dataset de prueba, pero que también \"generalize\" bien para ejemplos\n",
    "# que no haya visto antes.\n",
    "\n",
    "# En esta sección separamos el dataset en 2 partes: el training set y test set\n",
    "# utilizamos la función train_test_split para que los set sean \"estratificados\"\n",
    "X_train_o, X_test_o, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Visualización de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26676a6e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizar los datos que vamos utilizar nos permite entender mejor el problema\n",
    "# en el caso de MNIST la tarea es compleja desde el punto de vista de la clasificación\n",
    "# pero los datos ya están perfectos para el tratamiento (sin outliers, valores faltantes, etc)\n",
    "fig=plt.figure()\n",
    "a=fig.add_subplot(2,2,1)\n",
    "plt.imshow(np.reshape(X_train_o[1], [28, 28]),cmap=\"gray\")\n",
    "a=fig.add_subplot(2,2,2)\n",
    "plt.imshow(np.reshape(X_train_o[2], [28, 28]),cmap=\"gray\")\n",
    "a=fig.add_subplot(2,2,3)\n",
    "plt.imshow(np.reshape(X_train_o[3], [28, 28]),cmap=\"gray\")\n",
    "a=fig.add_subplot(2,2,4)\n",
    "plt.imshow(np.reshape(X_train_o[4], [28, 28]),cmap=\"gray\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Normalización de los datos (aka Feature Engineering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# Antes de enviar los datos al modelo, hacemos una adaptación de los datos para normalizarlos\n",
    "# Por normalización en este contexto la idea es remover la media de cada \"feature\"\n",
    "# (i.e. centrar en zero) y dividir por la \"variance\", es decir hacer que los datos estén entre\n",
    "# 0 y 1.\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train_o)\n",
    "X_test = scaler.transform(X_test_o)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regresión Logistica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 12.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='multinomial',\n",
       "          n_jobs=1, penalty='l2', random_state=42, solver='saga', tol=0.1,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El problema que vamos a classificar tiene 768 dimensiones de entrada (28x28)\n",
    "# La regresión logistica intentará definir una \"línea\" en este espacio multidimensional\n",
    "# que mejor represente el dato\n",
    "\n",
    "# Creamos el modelo que queremos usar\n",
    "# Para la regresión logistica, hemos indicado que el modelo es para una\n",
    "# clasificacion de multiplas clases\n",
    "lr_clf = LogisticRegression(multi_class='multinomial', solver='saga', random_state=42, tol=0.1)\n",
    "\n",
    "# Hacemos fit del modelo al dato de training\n",
    "%time lr_clf.fit(X_train, y_train)\n",
    "\n",
    "# Nosotros medimos el tiempo de entrenamiento porque es un parametro para entender el rendimiento\n",
    "# del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Evaluación del Modelo\n",
    "\n",
    "- Exactitud - Accuracy\n",
    "- *Confusion Matrix*\n",
    "\n",
    "  - Precisión - Alta precision, pocos falsos positivos - $P=\\frac{T_{P}}{T_{P}+F_{P}}$\n",
    "\n",
    " - \"Retirada\" - Alto *Recall*, pocos falsos negativos - $R=\\frac{T_{P}}{T_{P}+F_{N}}$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Evaluación del Modelo - Exactitud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 128 ms\n",
      "Rendimiento en el dataset de training: 0.8895\n",
      "Wall time: 49.7 s\n",
      "Rendimiento en la cross validación: 0.8822\n",
      "Wall time: 24.6 ms\n",
      "Rendimiento en el dataset de pruebas: 0.8945\n"
     ]
    }
   ],
   "source": [
    "# Los modelos de Scikit-Learn ya vienen con funciones para evaluar el rendimiento del modelo\n",
    "# Con este modelo sencillo hemos podido alcanzar una \"exactitud\" de mis previsiones de casi 90%\n",
    "# Usamos \"exactitud\" para diferenciar de precisión que significa otra cosa en nuestras medidas\n",
    "# rendimiento.\n",
    "\n",
    "# Comparar la exactitud entre el set de entrenamiento y el de pruebas nos permite entender si\n",
    "# esta ocurriendo overfitting\n",
    "%time train_score = lr_clf.score(X_train, y_train)\n",
    "print(\"Rendimiento en el dataset de training: %.4f\" % train_score)\n",
    "%time cv_score = cross_val_score(lr_clf, X_train, y_train, cv=5)\n",
    "print(\"Rendimiento en la cross validación: %.4f\" % cv_score.mean())\n",
    "%time score = lr_clf.score(X_test, y_test)\n",
    "print(\"Rendimiento en el dataset de pruebas: %.4f\" % score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Evaluación del Modelo - *Confusion Matrix*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Esta función esta adaptada de la documentación del Scikit-Learn para presentar\n",
    "# la matrix de \"confusión\" de forma más visual\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        #print(\"Normalized confusion matrix\")\n",
    "    #else:\n",
    "        #print('Confusion matrix, without normalization')\n",
    "        \n",
    "\n",
    "    #print(cm)\n",
    "    \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel(\"Valor real\")\n",
    "    plt.xlabel(\"Predicción\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1uWFYGovf2M4ZUxcxatZLPPibXgDMf30rPadk0HNKBuPvf4Wdn3/Lezvyqz2j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\n0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2667e63b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# La confusion matrix nos presenta una información sumaria de la precisión y del recall\n",
    "cm=confusion_matrix(y_test, lr_clf.predict(X_test))\n",
    "plt.figure(figsize=(8,8))\n",
    "plot_confusion_matrix(cm, classes=[\"zero\",\"uno\",\"dos\",\"tres\",\"cuatro\",\"cinco\",\"seis\",\"siete\",\"ocho\",\"nueve\"], normalize=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}