{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7.5\n",
      "NumPy version: 1.18.1\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "\n",
    "print(f\"Python version: {platform.python_version()}\")\n",
    "assert platform.python_version_tuple() >= (\"3\", \"6\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "print(f\"NumPy version: {np.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# Setup plots\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = 10, 8\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scikit-learn version: 0.22.1\n"
     ]
    }
   ],
   "source": [
    "# Import ML packages\n",
    "import sklearn\n",
    "\n",
    "print(f\"scikit-learn version: {sklearn.__version__}\")\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import plot_confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(pred_func, X, y, figure=None):\n",
    "    \"\"\"Plot a decision boundary\"\"\"\n",
    "\n",
    "    if figure is None:  # If no figure is given, create a new one\n",
    "        plt.figure()\n",
    "    # Set min and max values and give it some padding\n",
    "    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
    "    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
    "    h = 0.01\n",
    "    # Generate a grid of points with distance h between them\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    # Predict the function value for the whole grid\n",
    "    Z = pred_func(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    # Plot the contour and training examples\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The k-nearest neighbors algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-NN in a nutshell\n",
    "\n",
    "- Simple, instance-based algorithm: prediction is based on the $k$ nearest neighbors of a data sample.\n",
    "- No model creation, training = storing samples.\n",
    "\n",
    "![K-NN](images/knn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Example: planar data classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (100, 2). y_train: (100,)\n"
     ]
    }
   ],
   "source": [
    "# Generate 2D data\n",
    "x_train, y_train = make_classification(\n",
    "    n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1\n",
    ")\n",
    "\n",
    "rng = np.random.RandomState(2)\n",
    "x_train += 2 * rng.uniform(size=x_train.shape)\n",
    "\n",
    "print(f\"x_train: {x_train.shape}. y_train: {y_train.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 597
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot generated data\n",
    "plt.scatter(x_train[:, 0], x_train[:, 1], marker=\"o\", c=y_train, s=25, edgecolor=\"k\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a K-NN classifier\n",
    "knn_2d_clf = KNeighborsClassifier(n_neighbors=5)\n",
    "knn_2d_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 597
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(lambda x: knn_2d_clf.predict(x), x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.97000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate  classifier\n",
    "train_acc = knn_2d_clf.score(x_train, y_train)\n",
    "\n",
    "print(f\"Training accuracy: {train_acc:.05f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Example: fruits classification\n",
    "\n",
    "#### Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-03-22 11:32:27--  http://www.eyrignoux.com.fr/coursIA/machineLearning/fruit_data_with_colors.txt\n",
      "Résolution de www.eyrignoux.com.fr (www.eyrignoux.com.fr)… 62.210.16.62\n",
      "Connexion à www.eyrignoux.com.fr (www.eyrignoux.com.fr)|62.210.16.62|:80… connecté.\n",
      "requête HTTP transmise, en attente de la réponse… 301 Moved Permanently\n",
      "Emplacement : https://www.eyrignoux.com.fr/coursIA/machineLearning/fruit_data_with_colors.txt [suivant]\n",
      "--2022-03-22 11:32:27--  https://www.eyrignoux.com.fr/coursIA/machineLearning/fruit_data_with_colors.txt\n",
      "Connexion à www.eyrignoux.com.fr (www.eyrignoux.com.fr)|62.210.16.62|:443… connecté.\n",
      "requête HTTP transmise, en attente de la réponse… 200 OK\n",
      "Taille : 2370 (2,3K) [text/plain]\n",
      "Sauvegarde en : « fruit_data_with_colors.txt »\n",
      "\n",
      "fruit_data_with_col 100%[===================>]   2,31K  --.-KB/s    in 0s      \n",
      "\n",
      "2022-03-22 11:32:29 (48,1 MB/s) — « fruit_data_with_colors.txt » sauvegardé [2370/2370]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download data as a text file\n",
    "!wget http://www.eyrignoux.com.fr/coursIA/machineLearning/fruit_data_with_colors.txt -O fruit_data_with_colors.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>spanish_jumbo</td>\n",
       "      <td>356</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>172</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>116</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>braeburn</td>\n",
       "      <td>178</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>golden_delicious</td>\n",
       "      <td>152</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>cripps_pink</td>\n",
       "      <td>156</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>150</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>lemon</td>\n",
       "      <td>unknown</td>\n",
       "      <td>118</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>orange</td>\n",
       "      <td>turkey_navel</td>\n",
       "      <td>154</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit_label fruit_name     fruit_subtype  mass  width  height  color_score\n",
       "0             1      apple      granny_smith   192    8.4     7.3         0.55\n",
       "25            3     orange     spanish_jumbo   356    9.2     9.2         0.75\n",
       "11            1      apple          braeburn   172    7.1     7.6         0.92\n",
       "56            4      lemon           unknown   116    5.9     8.1         0.73\n",
       "8             1      apple          braeburn   178    7.1     7.8         0.92\n",
       "14            1      apple  golden_delicious   152    7.6     7.3         0.69\n",
       "21            1      apple       cripps_pink   156    7.4     7.4         0.84\n",
       "35            3     orange      turkey_navel   150    7.1     7.9         0.75\n",
       "58            4      lemon           unknown   118    6.1     8.1         0.70\n",
       "37            3     orange      turkey_navel   154    7.3     7.3         0.79"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data into a DataFrame\n",
    "fruits = pd.read_table(\"fruit_data_with_colors.txt\")\n",
    "\n",
    "# Show 10 random samples\n",
    "fruits.sample(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apples : 32.2%\n",
      "mandarins : 8.5%\n",
      "oranges : 32.2%\n",
      "lemons : 27.1%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate class distribution\n",
    "samples_count = fruits.size\n",
    "for name in fruits[\"fruit_name\"].unique():\n",
    "    class_percent = fruits[fruits.fruit_name == name].size / samples_count\n",
    "    print(f\"{name}s : {class_percent * 100:.1f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: (59, 3). y: (59,)\n"
     ]
    }
   ],
   "source": [
    "# For this scenario, we use only the mass, width, and height features of each fruit instance\n",
    "x = fruits[[\"mass\", \"width\", \"height\"]]\n",
    "# Objective is to predict the fruit class\n",
    "y = fruits[\"fruit_label\"]\n",
    "\n",
    "print(f\"x: {x.shape}. y: {y.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (47, 3). y_train: (47,)\n",
      "x_test: (12, 3). y_test: (12,)\n"
     ]
    }
   ],
   "source": [
    "# Split data between training and test sets with a 80/20 ratio\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "print(f\"x_train: {x_train.shape}. y_train: {y_train.shape}\")\n",
    "print(f\"x_test: {x_test.shape}. y_test: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize data\n",
    "scaler = StandardScaler().fit(x_train)\n",
    "\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Classifier creation and \"training\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# k = 5\n",
    "knn_fruits_clf = KNeighborsClassifier(n_neighbors=5)\n",
    "knn_fruits_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Classifier evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.89362\n",
      "Test accuracy: 0.75000\n"
     ]
    }
   ],
   "source": [
    "# Compute accuracy on training and test sets\n",
    "train_acc = knn_fruits_clf.score(x_train, y_train)\n",
    "test_acc = knn_fruits_clf.score(x_test, y_test)\n",
    "\n",
    "print(f\"Training accuracy: {train_acc:.05f}\")\n",
    "print(f\"Test accuracy: {test_acc:.05f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 499,
       "width": 594
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the confusion matrix for test data\n",
    "with sns.axes_style(\"white\"):  # Temporary hide Seaborn grid lines\n",
    "    display = plot_confusion_matrix(\n",
    "        knn_fruits_clf,\n",
    "        x_test,\n",
    "        y_test,\n",
    "        display_labels=fruits[\"fruit_name\"].unique(),\n",
    "        cmap=plt.cm.Blues,\n",
    "    )\n",
    "    display.ax_.set_title(\"Confusion matrix for fruit classification\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.67      0.80      0.73         5\n",
      "           3       0.67      0.50      0.57         4\n",
      "           4       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           0.75        12\n",
      "   macro avg       0.78      0.77      0.77        12\n",
      "weighted avg       0.75      0.75      0.74        12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute classification metrics\n",
    "print(classification_report(y_test, knn_fruits_clf.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Using the classifier for predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a mapping from fruit label value to fruit name to make results easier to interpret\n",
    "lookup_fruit_name = dict(zip(fruits.fruit_label.unique(), fruits.fruit_name.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'orange'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first example: a small fruit with mass 20g, width 4.3 cm, height 5.5 cm\n",
    "fruit_prediction = knn_fruits_clf.predict([[20, 4.3, 5.5]])\n",
    "lookup_fruit_name[fruit_prediction[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'orange'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second example: a larger, elongated fruit with mass 100g, width 6.3 cm, height 8.5 cm\n",
    "fruit_prediction = knn_fruits_clf.predict([[100, 6.3, 8.5]])\n",
    "lookup_fruit_name[fruit_prediction[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Importance of the `k` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_range = range(1, 20)\n",
    "scores = []\n",
    "\n",
    "# Train several classifiers with different values for k\n",
    "for k in k_range:\n",
    "    knn_clf = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn_clf.fit(x_train, y_train)\n",
    "    scores.append(knn_clf.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 624
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results\n",
    "plt.figure()\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.scatter(k_range, scores)\n",
    "plt.xticks([0,5,10,15,20]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": [
     "hide-input"
    ]
   },
   "source": [
    "TODO\n",
    "\n",
    "- Planar data: add multiclass data\n",
    "- Better looking graphs: https://stackabuse.com/k-nearest-neighbors-algorithm-in-python-and-scikit-learn/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Diaporama",
  "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}