cap11/CH11EJ2-Maquinas-de-soporte-vectorial-Py.ipynb

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   "source": [
    "<div style=\"width: 100%; clear: both;\">\n",
    "<div style=\"float: left; width: 50%;\">\n",
    "<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\" align=\"left\">\n",
    "</div>\n",
    "<div style=\"float: right; width: 50%;\">\n",
    "<p style=\"margin: 0; padding-top: 22px; text-align:right;\"><a href=\"http://www.editorialuoc.cat/mineria-de-datos\" target=\"_blank\">Minería de datos: Modelos y algoritmos</a></p>\n",
    "<p style=\"margin: 0; text-align:right;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Universitat Oberta de Catalunya (<b>UOC</b>)</p>\n",
    "</div>\n",
    "</div>\n",
    "<div style=\"width:100%;\">&nbsp;</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejemplo de clasificación mediante SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Carga de los datos\n",
    "\n",
    "En primer lugar, cargaremos el conjunto de datos IRIS dataset (Más información en https://archive.ics.uci.edu/ml/datasets/iris). Éste se puede descargar de Internet o puede ser cargado directamente de la librería `scikit-learn`, que incorpora un conjunto de datasets muy conocidos y empleados para minería de datos y machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "# import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "class_names = iris.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Preparación del conjunto de entrenamiento y test\n",
    "\n",
    "Generamos los conjuntos de entrenamiento y test mediante la librería `train_test_split`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into a training set and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Creación y entrenamiento del modelo\n",
    "\n",
    "Las siguientes líneas de código crean y entrenan un modelo basado en SVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.1, kernel='linear')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "# Run classifier\n",
    "classifier = svm.SVC(kernel='linear', C=0.1)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Validación del modelo\n",
    "\n",
    "En primer lugar, comprovaremos la precisión sobre el conjunto de datos de test. Y seguidamente, crearemos la matriz de confusión asociada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13  0  0]\n",
      " [ 0 15  1]\n",
      " [ 0  0  9]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import numpy as np\n",
    "\n",
    "# Prediction on test data\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, y_pred)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(cnf_matrix)"
   ]
  }
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