Exercicios/ex06_K_Nearest_Neighbors.ipynb

{
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   "execution_count": 30,
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   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "iris = load_iris()\n",
    "x = pd.DataFrame(iris.data, columns=[iris.feature_names])\n",
    "y = pd.Series(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
       "0               5.1              3.5               1.4              0.2\n",
       "1               4.9              3.0               1.4              0.2\n",
       "2               4.7              3.2               1.3              0.2\n",
       "3               4.6              3.1               1.5              0.2\n",
       "4               5.0              3.6               1.4              0.2"
      ]
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     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhor Acurácia :: 0.9733333333333334\n",
      "Melhor K :: 6\n",
      "Método distância :: minkowski\n",
      "Melhor valor p :: 3\n"
     ]
    }
   ],
   "source": [
    "# Normalizando as variáveis preditoras:\n",
    "normalizador = MinMaxScaler(feature_range = (0, 1))\n",
    "X_norm = normalizador.fit_transform(x)\n",
    "\n",
    "# Definindo os valores que serão testados no KNN:\n",
    "valores_k = np.array([1, 2, 3, 4, 6, 5, 7, 12, 10])\n",
    "calculo_distancia = [\"minkowski\", \"chebyshev\"]\n",
    "valores_p = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "valores_grid = {'n_neighbors':valores_k, 'metric':calculo_distancia, 'p':valores_p}\n",
    "\n",
    "#Criação do modelo:\n",
    "modelo = KNeighborsClassifier()\n",
    "\n",
    "# Criando os grid:\n",
    "gridKNN = GridSearchCV(estimator = modelo, param_grid = valores_grid, cv=5)\n",
    "gridKNN.fit(X_norm, y)\n",
    "\n",
    "# Imprimindo os melhor parâmetros:\n",
    "print(\"Melhor Acurácia ::\", gridKNN.best_score_)\n",
    "print(\"Melhor K ::\", gridKNN.best_estimator_.n_neighbors)\n",
    "print(\"Método distância ::\", gridKNN.best_estimator_.metric)\n",
    "print(\"Melhor valor p ::\", gridKNN.best_estimator_.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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