lectures/04_kNNs-SVM-RBF.ipynb

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   "source": [
    "![](img/330-banner.png)"
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   "source": [
    "# Lecture 4: $k$-Nearest Neighbours and SVM RBFs  \n",
    "\n",
    "UBC 2022-23\n",
    "\n",
    "Instructor: Varada Kolhatkar\n",
    "\n",
    "> If two things are similar, the thought of one will tend to trigger the thought of the other <br>\n",
    "-- Aristotle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports, announcements, and LOs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Imports"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
    "slideshow": {
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   "source": [
    "import sys\n",
    "\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "\n",
    "sys.path.append(\"code/.\")\n",
    "\n",
    "import ipywidgets as widgets\n",
    "import mglearn\n",
    "from IPython.display import display\n",
    "from ipywidgets import interact, interactive\n",
    "from plotting_functions import *\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.model_selection import cross_validate, train_test_split\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 200)\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Announcements\n",
    "\n",
    "- hw2 deadline is today at 11:59pm. \n",
    "- Class add/drop deadline is today. \n",
    "- hw3 will be released later today.\n",
    "    - The due date is going to be Monday, Oct 3. \n",
    "    - I plan to allow group submission starting this homework. \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Learning outcomes\n",
    "\n",
    "From this lecture, you will be able to \n",
    "\n",
    "- explain the notion of similarity-based algorithms; \n",
    "- broadly describe how $k$-NNs use distances; \n",
    "- discuss the effect of using a small/large value of the hyperparameter $k$ when using the $k$-NN algorithm; \n",
    "- describe the problem of curse of dimensionality; \n",
    "- explain the general idea of SVMs with RBF kernel;\n",
    "- broadly describe the relation of `gamma` and `C` hyperparameters of SVMs with the fundamental tradeoff."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
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    "tags": []
   },
   "source": [
    "### Quick recap\n",
    "\n",
    "- Why do we split the data? \n",
    "- What are the 4 types of data we discussed last class?\n",
    "- What are the advantages of cross-validation?\n",
    "- What is overfitting? \n",
    "- What's the fundamental trade-off in supervised machine learning? \n",
    "- What is the golden rule of machine learning?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
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   "cell_type": "markdown",
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     "slide_type": "slide"
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   },
   "source": [
    "```{important} \n",
    "If you want to run this notebook you will have to install `ipywidgets`. \n",
    "Follow the installation instructions [here](https://ipywidgets.readthedocs.io/en/latest/user_install.html).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Motivation and distances [[video](https://youtu.be/hCa3EXEUmQk)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "### Analogy-based models\n",
    "\n",
    "- Suppose you are given the following training examples with corresponding labels and are asked to label a given test example.\n",
    "\n",
    "![](img/knn-motivation.png)\n",
    "<!-- <img src='./img/knn-motivation.png' width=\"1000\"> -->\n",
    "\n",
    "[source](https://vipl.ict.ac.cn/en/database.php)\n",
    "\n",
    "- An intuitive way to classify the test example is by finding the most \"similar\" example(s) from the training set and using that label for the test example.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Analogy-based algorithms in practice\n",
    "\n",
    "- [Herta's High-tech Facial Recognition](https://www.hertasecurity.com/en)\n",
    "    - Feature vectors for human faces \n",
    "    - $k$-NN to identify which face is on their watch list\n",
    "- Recommendation systems     \n",
    "\n",
    "![](img/hetra.png)\n",
    "<!-- <img src='./img/hetra.png' width=\"500\"> -->\n",
    "\n",
    "[source](https://hertasecurity.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### General idea of $k$-nearest neighbours algorithm\n",
    "\n",
    "- Consider the following toy dataset with two classes.\n",
    "    - blue circles $\\rightarrow$ class 0\n",
    "    - red triangles $\\rightarrow$ class 1 \n",
    "    - green stars $\\rightarrow$ test examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [],
   "source": [
    "X, y = mglearn.datasets.make_forge()\n",
    "X_test = np.array([[8.2, 3.66214339], [9.9, 3.2], [11.2, 0.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_test_points(X, y, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Given a new data point, predict the class of the data point by finding the \"closest\" data point in the training set, i.e., by finding its \"nearest neighbour\" or majority vote of nearest neighbours. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def f(n_neighbors):\n",
    "    return plot_knn_clf(X, y, X_test, n_neighbors=n_neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b03aeec4034b4de5b0381df868af8733",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='n_neighbors', max=10, min=1, step=2), Output()), _dom_cl…"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interactive(\n",
    "    f,\n",
    "    n_neighbors=widgets.IntSlider(min=1, max=10, step=2, value=1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Geometric view of tabular data and dimensions \n",
    "\n",
    "- To understand analogy-based algorithms it's useful to think of data as points in a high dimensional space. \n",
    "- Our `X` represents the problem in terms of relevant **features** ($d$) with one dimension for each **feature** (column).\n",
    "- Examples are **points in a $d$-dimensional space**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "How many dimensions (features) are there in the cities data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "cities_df = pd.read_csv(\"data/canada_usa_cities.csv\")\n",
    "X_cities = cities_df[[\"longitude\", \"latitude\"]]\n",
    "y_cities = cities_df[\"country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.discrete_scatter(X_cities.iloc[:, 0], X_cities.iloc[:, 1], y_cities)\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"latitude\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Recall the [Spotify Song Attributes](https://www.kaggle.com/geomack/spotifyclassification/home) dataset from homework 1. \n",
    "- How many dimensions (features) we used in the homework? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of features in the Spotify dataset: 13\n"
     ]
    },
    {
     "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>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.833</td>\n",
       "      <td>204600</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.743</td>\n",
       "      <td>326933</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.838</td>\n",
       "      <td>185707</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>-7.148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>75.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.494</td>\n",
       "      <td>199413</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>-15.236</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>86.468</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.678</td>\n",
       "      <td>392893</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>-11.648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>174.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  duration_ms  energy  instrumentalness  key  \\\n",
       "0        0.0102         0.833       204600   0.434          0.021900    2   \n",
       "1        0.1990         0.743       326933   0.359          0.006110    1   \n",
       "2        0.0344         0.838       185707   0.412          0.000234    2   \n",
       "3        0.6040         0.494       199413   0.338          0.510000    5   \n",
       "4        0.1800         0.678       392893   0.561          0.512000    5   \n",
       "\n",
       "   liveness  loudness  mode  speechiness    tempo  time_signature  valence  \n",
       "0    0.1650    -8.795     1       0.4310  150.062             4.0    0.286  \n",
       "1    0.1370   -10.401     1       0.0794  160.083             4.0    0.588  \n",
       "2    0.1590    -7.148     1       0.2890   75.044             4.0    0.173  \n",
       "3    0.0922   -15.236     1       0.0261   86.468             4.0    0.230  \n",
       "4    0.4390   -11.648     0       0.0694  174.004             4.0    0.904  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spotify_df = pd.read_csv(\"data/spotify.csv\", index_col=0)\n",
    "X_spotify = spotify_df.drop(columns=[\"target\", \"song_title\", \"artist\"])\n",
    "print(\"The number of features in the Spotify dataset: %d\" % X_spotify.shape[1])\n",
    "X_spotify.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Dimensions in ML problems \n",
    "\n",
    "In ML, usually we deal with high dimensional problems where examples are hard to visualize.  \n",
    "\n",
    "- $d \\approx 20$ is considered low dimensional\n",
    "- $d \\approx 1000$ is considered medium dimensional \n",
    "- $d \\approx 100,000$ is considered high dimensional "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Feature vectors \n",
    "\n",
    "**Feature vector**\n",
    ": is composed of feature values associated with an example.\n",
    "\n",
    "Some example feature vectors are shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An example feature vector from the cities dataset: [-130.0437   55.9773]\n",
      "An example feature vector from the Spotify dataset: \n",
      "[ 1.02000e-02  8.33000e-01  2.04600e+05  4.34000e-01  2.19000e-02\n",
      "  2.00000e+00  1.65000e-01 -8.79500e+00  1.00000e+00  4.31000e-01\n",
      "  1.50062e+02  4.00000e+00  2.86000e-01]\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"An example feature vector from the cities dataset: %s\"\n",
    "    % (X_cities.iloc[0].to_numpy())\n",
    ")\n",
    "print(\n",
    "    \"An example feature vector from the Spotify dataset: \\n%s\"\n",
    "    % (X_spotify.iloc[0].to_numpy())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Similarity between examples\n",
    "\n",
    "Let's take 2 points (two feature vectors) from the cities dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>-104.8253</td>\n",
       "      <td>38.8340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-112.0741</td>\n",
       "      <td>33.4484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude\n",
       "69  -104.8253   38.8340\n",
       "35  -112.0741   33.4484"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_cities = X_cities.sample(2, random_state=120)\n",
    "two_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The two sampled points are shown as big black circles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.discrete_scatter(\n",
    "    X_cities.iloc[:, 0], X_cities.iloc[:, 1], y_cities, s=8, alpha=0.3\n",
    ")\n",
    "mglearn.discrete_scatter(\n",
    "    two_cities.iloc[:, 0], two_cities.iloc[:, 1], markers=\"o\", c=\"k\", s=18\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Distance between feature vectors \n",
    "\n",
    "- For the cities at the two big circles, what is the _distance_ between them?\n",
    "- A common way to calculate the distance between vectors is calculating the **Euclidean distance**. \n",
    "- The euclidean distance between vectors $u = <u_1, u_2, \\dots, u_n>$ and $v = <v_1, v_2, \\dots, v_n>$ is defined as: \n",
    "\n",
    "$$distance(u, v) = \\sqrt{\\sum_{i =1}^{n} (u_i - v_i)^2}$$ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Euclidean distance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>-104.8253</td>\n",
       "      <td>38.8340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-112.0741</td>\n",
       "      <td>33.4484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude\n",
       "69  -104.8253   38.8340\n",
       "35  -112.0741   33.4484"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- Subtract the two cities\n",
    "- Square the difference\n",
    "- Sum them up \n",
    "- Take the square root "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subtract the cities: \n",
      "longitude   -7.2488\n",
      "latitude    -5.3856\n",
      "dtype: float64\n",
      "\n",
      "Sum of squares: 81.5498\n",
      "Euclidean distance between cities: 9.0305\n"
     ]
    }
   ],
   "source": [
    "# Subtract the two cities\n",
    "print(\"Subtract the cities: \\n%s\\n\" % (two_cities.iloc[1] - two_cities.iloc[0]))\n",
    "\n",
    "# Squared sum of the difference\n",
    "print(\n",
    "    \"Sum of squares: %0.4f\" % (np.sum((two_cities.iloc[1] - two_cities.iloc[0]) ** 2))\n",
    ")\n",
    "\n",
    "# Take the square root\n",
    "print(\n",
    "    \"Euclidean distance between cities: %0.4f\"\n",
    "    % (np.sqrt(np.sum((two_cities.iloc[1] - two_cities.iloc[0]) ** 2)))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
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       "      <td>33.4484</td>\n",
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      ],
      "text/plain": [
       "    longitude  latitude\n",
       "69  -104.8253   38.8340\n",
       "35  -112.0741   33.4484"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 9.03049217],\n",
       "       [9.03049217, 0.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Euclidean distance using sklearn\n",
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "\n",
    "euclidean_distances(two_cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Note: `scikit-learn` supports a number of other [distance metrics](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Finding the nearest neighbour\n",
    "\n",
    "- Let's look at distances from all cities to all other cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(209, 209)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dists = euclidean_distances(X_cities)\n",
    "np.fill_diagonal(dists, np.inf)\n",
    "dists.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>21.656349</td>\n",
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       "      <td>20.528403</td>\n",
       "      <td>25.525757</td>\n",
       "      <td>25.873103</td>\n",
       "      <td>28.479109</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277381</td>\n",
       "      <td>51.076798</td>\n",
       "      <td>10.783789</td>\n",
       "      <td>49.169693</td>\n",
       "      <td>51.934205</td>\n",
       "      <td>50.483751</td>\n",
       "      <td>56.512897</td>\n",
       "      <td>44.235152</td>\n",
       "      <td>50.249720</td>\n",
       "      <td>43.699224</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.106452</td>\n",
       "      <td>14.935802</td>\n",
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       "      <td>inf</td>\n",
       "      <td>0.474552</td>\n",
       "      <td>10.781004</td>\n",
       "      <td>20.194002</td>\n",
       "      <td>25.191396</td>\n",
       "      <td>25.538702</td>\n",
       "      <td>28.144750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.275352</td>\n",
       "      <td>50.743133</td>\n",
       "      <td>10.480249</td>\n",
       "      <td>48.836189</td>\n",
       "      <td>51.599860</td>\n",
       "      <td>50.150395</td>\n",
       "      <td>56.179123</td>\n",
       "      <td>43.904226</td>\n",
       "      <td>49.916254</td>\n",
       "      <td>43.365623</td>\n",
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       "      <th>4</th>\n",
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       "      <td>19.719500</td>\n",
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       "      <td>25.064200</td>\n",
       "      <td>27.670344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675814</td>\n",
       "      <td>50.269880</td>\n",
       "      <td>10.051472</td>\n",
       "      <td>48.363192</td>\n",
       "      <td>51.125476</td>\n",
       "      <td>49.677629</td>\n",
       "      <td>55.705696</td>\n",
       "      <td>43.435186</td>\n",
       "      <td>49.443317</td>\n",
       "      <td>42.892477</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>58.289799</td>\n",
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       "      <td>50.483751</td>\n",
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       "      <td>50.333340</td>\n",
       "      <td>0.873356</td>\n",
       "      <td>41.380643</td>\n",
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       "      <td>6.102435</td>\n",
       "      <td>6.957987</td>\n",
       "      <td>0.316363</td>\n",
       "      <td>6.800190</td>\n",
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       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>64.183423</td>\n",
       "      <td>68.887343</td>\n",
       "      <td>56.512897</td>\n",
       "      <td>56.179123</td>\n",
       "      <td>55.705696</td>\n",
       "      <td>45.418031</td>\n",
       "      <td>36.031385</td>\n",
       "      <td>31.032874</td>\n",
       "      <td>30.709185</td>\n",
       "      <td>28.088948</td>\n",
       "      <td>...</td>\n",
       "      <td>56.358333</td>\n",
       "      <td>5.442806</td>\n",
       "      <td>47.259286</td>\n",
       "      <td>7.369875</td>\n",
       "      <td>5.108681</td>\n",
       "      <td>6.102435</td>\n",
       "      <td>inf</td>\n",
       "      <td>12.950733</td>\n",
       "      <td>6.303916</td>\n",
       "      <td>12.819584</td>\n",
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       "      <th>206</th>\n",
       "      <td>52.426410</td>\n",
       "      <td>57.253724</td>\n",
       "      <td>44.235152</td>\n",
       "      <td>43.904226</td>\n",
       "      <td>43.435186</td>\n",
       "      <td>33.258427</td>\n",
       "      <td>24.059863</td>\n",
       "      <td>19.187663</td>\n",
       "      <td>18.932124</td>\n",
       "      <td>16.380495</td>\n",
       "      <td>...</td>\n",
       "      <td>44.100248</td>\n",
       "      <td>7.767852</td>\n",
       "      <td>35.637982</td>\n",
       "      <td>5.930561</td>\n",
       "      <td>9.731583</td>\n",
       "      <td>6.957987</td>\n",
       "      <td>12.950733</td>\n",
       "      <td>inf</td>\n",
       "      <td>6.837848</td>\n",
       "      <td>3.322755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>58.033459</td>\n",
       "      <td>62.771969</td>\n",
       "      <td>50.249720</td>\n",
       "      <td>49.916254</td>\n",
       "      <td>49.443317</td>\n",
       "      <td>39.167214</td>\n",
       "      <td>29.799983</td>\n",
       "      <td>24.810368</td>\n",
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       "      <td>21.878183</td>\n",
       "      <td>...</td>\n",
       "      <td>50.098326</td>\n",
       "      <td>0.930123</td>\n",
       "      <td>41.121628</td>\n",
       "      <td>1.082749</td>\n",
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       "      <td>0.316363</td>\n",
       "      <td>6.303916</td>\n",
       "      <td>6.837848</td>\n",
       "      <td>inf</td>\n",
       "      <td>6.555740</td>\n",
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       "    <tr>\n",
       "      <th>208</th>\n",
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       "      <td>43.699224</td>\n",
       "      <td>43.365623</td>\n",
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       "      <td>23.244592</td>\n",
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       "      <td>5.473691</td>\n",
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       "      <td>12.819584</td>\n",
       "      <td>3.322755</td>\n",
       "      <td>6.555740</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 209 columns</p>\n",
       "</div>"
      ],
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       "           0          1          2          3          4          5    \\\n",
       "0          inf   4.955113   9.869531  10.106452  10.449666  19.381676   \n",
       "1     4.955113        inf  14.677579  14.935802  15.305346  24.308448   \n",
       "2     9.869531  14.677579        inf   0.334411   0.808958  11.115406   \n",
       "3    10.106452  14.935802   0.334411        inf   0.474552  10.781004   \n",
       "4    10.449666  15.305346   0.808958   0.474552        inf  10.306500   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "204  58.289799  63.032656  50.483751  50.150395  49.677629  39.405415   \n",
       "205  64.183423  68.887343  56.512897  56.179123  55.705696  45.418031   \n",
       "206  52.426410  57.253724  44.235152  43.904226  43.435186  33.258427   \n",
       "207  58.033459  62.771969  50.249720  49.916254  49.443317  39.167214   \n",
       "208  51.498562  56.252160  43.699224  43.365623  42.892477  32.612755   \n",
       "\n",
       "           6          7          8          9    ...        199        200  \\\n",
       "0    28.366626  33.283857  33.572105  36.180388  ...   9.834455  58.807684   \n",
       "1    33.200978  38.082949  38.359992  40.957919  ...  14.668787  63.533498   \n",
       "2    20.528403  25.525757  25.873103  28.479109  ...   0.277381  51.076798   \n",
       "3    20.194002  25.191396  25.538702  28.144750  ...   0.275352  50.743133   \n",
       "4    19.719500  24.716985  25.064200  27.670344  ...   0.675814  50.269880   \n",
       "..         ...        ...        ...        ...  ...        ...        ...   \n",
       "204  30.043890  25.057003  24.746328  22.127878  ...  50.333340   0.873356   \n",
       "205  36.031385  31.032874  30.709185  28.088948  ...  56.358333   5.442806   \n",
       "206  24.059863  19.187663  18.932124  16.380495  ...  44.100248   7.767852   \n",
       "207  29.799983  24.810368  24.497386  21.878183  ...  50.098326   0.930123   \n",
       "208  23.244592  18.256813  17.946783  15.328953  ...  43.546610   7.378764   \n",
       "\n",
       "           201        202        203        204        205        206  \\\n",
       "0    16.925593  56.951696  59.384127  58.289799  64.183423  52.426410   \n",
       "1    21.656349  61.691640  64.045304  63.032656  68.887343  57.253724   \n",
       "2    10.783789  49.169693  51.934205  50.483751  56.512897  44.235152   \n",
       "3    10.480249  48.836189  51.599860  50.150395  56.179123  43.904226   \n",
       "4    10.051472  48.363192  51.125476  49.677629  55.705696  43.435186   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "204  41.380643   1.345136   3.373031        inf   6.102435   6.957987   \n",
       "205  47.259286   7.369875   5.108681   6.102435        inf  12.950733   \n",
       "206  35.637982   5.930561   9.731583   6.957987  12.950733        inf   \n",
       "207  41.121628   1.082749   3.286821   0.316363   6.303916   6.837848   \n",
       "208  34.596810   5.473691   8.568009   6.800190  12.819584   3.322755   \n",
       "\n",
       "           207        208  \n",
       "0    58.033459  51.498562  \n",
       "1    62.771969  56.252160  \n",
       "2    50.249720  43.699224  \n",
       "3    49.916254  43.365623  \n",
       "4    49.443317  42.892477  \n",
       "..         ...        ...  \n",
       "204   0.316363   6.800190  \n",
       "205   6.303916  12.819584  \n",
       "206   6.837848   3.322755  \n",
       "207        inf   6.555740  \n",
       "208   6.555740        inf  \n",
       "\n",
       "[209 rows x 209 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dists)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's look at the distances between City 0 and some other cities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature vector for city 0: \n",
      "longitude   -130.0437\n",
      "latitude      55.9773\n",
      "Name: 0, dtype: float64\n",
      "\n",
      "Distances from city 0 to the first 5 cities: [        inf  4.95511263  9.869531   10.10645223 10.44966612]\n",
      "The closest city from city 0 is: 81 \n",
      "\n",
      "with feature vector: \n",
      "longitude   -129.9912\n",
      "latitude      55.9383\n",
      "Name: 81, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature vector for city 0: \\n%s\\n\" % (X_cities.iloc[0]))\n",
    "print(\"Distances from city 0 to the first 5 cities: %s\" % (dists[0][:5]))\n",
    "# We can find the closest city with `np.argmin`:\n",
    "print(\n",
    "    \"The closest city from city 0 is: %d \\n\\nwith feature vector: \\n%s\"\n",
    "    % (np.argmin(dists[0]), X_cities.iloc[np.argmin(dists[0])])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so the closest city to City 0 is City 81. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Question\n",
    "\n",
    "- Why did we set the diagonal entries to infinity before finding the closest city?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Finding the distances to a query point\n",
    "\n",
    "We can also find the distances to a new \"test\" or \"query\" city:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[58.85545875],\n",
       "       [63.80062924],\n",
       "       [49.30530902],\n",
       "       [49.01473536],\n",
       "       [48.60495488],\n",
       "       [39.96834506],\n",
       "       [32.92852376],\n",
       "       [29.53520104],\n",
       "       [29.52881619],\n",
       "       [27.84679073]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find a city that's closest to the a query city\n",
    "query_point = [[-80, 25]]\n",
    "\n",
    "dists = euclidean_distances(X_cities, query_point)\n",
    "dists[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The query point [[-80, 25]] is closest to the city with index 72 and the distance between them is: 0.7982\n"
     ]
    }
   ],
   "source": [
    "# The query point is closest to\n",
    "print(\n",
    "    \"The query point %s is closest to the city with index %d and the distance between them is: %0.4f\"\n",
    "    % (query_point, np.argmin(dists), dists[np.argmin(dists)])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## $k$-Nearest Neighbours ($k$-NNs) [[video](https://youtu.be/bENDqXKJLmg)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "small_cities = cities_df.sample(30, random_state=90)\n",
    "one_city = small_cities.sample(1, random_state=44)\n",
    "small_train_df = pd.concat([small_cities, one_city]).drop_duplicates(keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_small_cities = small_train_df.drop(columns=[\"country\"]).to_numpy()\n",
    "y_small_cities = small_train_df[\"country\"].to_numpy()\n",
    "test_point = one_city[[\"longitude\", \"latitude\"]].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_test_points(\n",
    "    X_small_cities,\n",
    "    y_small_cities,\n",
    "    test_point,\n",
    "    class_names=[\"Canada\", \"USA\"],\n",
    "    test_format=\"circle\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Given a new data point, predict the class of the data point by finding the \"closest\" data point in the training set, i.e., by finding its \"nearest neighbour\" or majority vote of nearest neighbours. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Suppose we want to predict the class of the black point.  \n",
    "- An intuitive way to do this is predict the same label as the \"closest\" point ($k = 1$) (1-nearest neighbour)\n",
    "- We would predict a target of **USA** in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_neighbors 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_knn_clf(\n",
    "    X_small_cities,\n",
    "    y_small_cities,\n",
    "    test_point,\n",
    "    n_neighbors=1,\n",
    "    class_names=[\"Canada\", \"USA\"],\n",
    "    test_format=\"circle\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "How about using $k > 1$ to get a more robust estimate? \n",
    "- For example, we could also use the 3 closest points (*k* = 3) and let them **vote** on the correct class.  \n",
    "- The **Canada** class would win in this case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_neighbors 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_knn_clf(\n",
    "    X_small_cities,\n",
    "    y_small_cities,\n",
    "    test_point,\n",
    "    n_neighbors=3,\n",
    "    class_names=[\"Canada\", \"USA\"],\n",
    "    test_format=\"circle\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction of the black dot with 1 neighbours: ['USA']\n",
      "Prediction of the black dot with 3 neighbours: ['Canada']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "k_values = [1, 3]\n",
    "\n",
    "for k in k_values:\n",
    "    neigh = KNeighborsClassifier(n_neighbors=k)\n",
    "    neigh.fit(X_small_cities, y_small_cities)\n",
    "    print(\n",
    "        \"Prediction of the black dot with %d neighbours: %s\"\n",
    "        % (k, neigh.predict(test_point))\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Choosing `n_neighbors`\n",
    "\n",
    "- The primary hyperparameter of the model is `n_neighbors` ($k$) which decides how many neighbours should vote during prediction? \n",
    "- What happens when we play around with `n_neighbors`?\n",
    "- Are we more likely to overfit with a low `n_neighbors` or a high `n_neighbors`?\n",
    "- Let's examine the effect of the hyperparameter on our cities data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "X = cities_df.drop(columns=[\"country\"])\n",
    "y = cities_df[\"country\"]\n",
    "\n",
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.1, random_state=123\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.001628</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.000988    0.001628    0.710526          1.0\n",
       "1  0.000674    0.001336    0.684211          1.0\n",
       "2  0.000673    0.000874    0.842105          1.0\n",
       "3  0.000650    0.000896    0.702703          1.0\n",
       "4  0.000540    0.000814    0.837838          1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 1\n",
    "knn1 = KNeighborsClassifier(n_neighbors=k)\n",
    "scores = cross_validate(knn1, X_train, y_train, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000671</td>\n",
       "      <td>0.030999</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.001435</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.602649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.602649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.000671    0.030999    0.605263     0.600000\n",
       "1  0.000557    0.001208    0.605263     0.600000\n",
       "2  0.000538    0.001182    0.605263     0.600000\n",
       "3  0.000507    0.001435    0.594595     0.602649\n",
       "4  0.000531    0.001276    0.594595     0.602649"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 100\n",
    "knn100 = KNeighborsClassifier(n_neighbors=k)\n",
    "scores = cross_validate(knn100, X_train, y_train, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8ea3c9f1fa443c78d026444a365e5f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='n_neighbors', max=101, min=1, step=5), Output()), _dom_c…"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(n_neighbors=1):\n",
    "    results = {}\n",
    "    knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    scores = cross_validate(knn, X_train, y_train, return_train_score=True)\n",
    "    results[\"n_neighbours\"] = [n_neighbors]\n",
    "    results[\"mean_train_score\"] = [round(scores[\"train_score\"].mean(), 3)]\n",
    "    results[\"mean_valid_score\"] = [round(scores[\"test_score\"].mean(), 3)]\n",
    "    print(pd.DataFrame(results))\n",
    "\n",
    "\n",
    "interactive(\n",
    "    f,\n",
    "    n_neighbors=widgets.IntSlider(min=1, max=101, step=5, value=1),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_knn_decision_boundaries(X_train, y_train, k_values=[1, 11, 100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### How to choose `n_neighbors`?\n",
    "\n",
    "- `n_neighbors` is a hyperparameter\n",
    "- We can use hyperparameter optimization to choose `n_neighbors`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "results_dict = {\n",
    "    \"n_neighbors\": [],\n",
    "    \"mean_train_score\": [],\n",
    "    \"mean_cv_score\": [],\n",
    "    \"std_cv_score\": [],\n",
    "    \"std_train_score\": [],\n",
    "}\n",
    "param_grid = {\"n_neighbors\": np.arange(1, 50, 5)}\n",
    "\n",
    "for k in param_grid[\"n_neighbors\"]:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_validate(knn, X_train, y_train, return_train_score=True)\n",
    "    results_dict[\"n_neighbors\"].append(k)\n",
    "\n",
    "    results_dict[\"mean_cv_score\"].append(np.mean(scores[\"test_score\"]))\n",
    "    results_dict[\"mean_train_score\"].append(np.mean(scores[\"train_score\"]))\n",
    "    results_dict[\"std_cv_score\"].append(scores[\"test_score\"].std())\n",
    "    results_dict[\"std_train_score\"].append(scores[\"train_score\"].std())\n",
    "\n",
    "results_df = pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>mean_train_score</th>\n",
       "      <th>mean_cv_score</th>\n",
       "      <th>std_cv_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_neighbors</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.755477</td>\n",
       "      <td>0.069530</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.792603</td>\n",
       "      <td>0.046020</td>\n",
       "      <td>0.013433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.819152</td>\n",
       "      <td>0.802987</td>\n",
       "      <td>0.041129</td>\n",
       "      <td>0.011336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.801863</td>\n",
       "      <td>0.782219</td>\n",
       "      <td>0.074141</td>\n",
       "      <td>0.008735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.777934</td>\n",
       "      <td>0.766430</td>\n",
       "      <td>0.062792</td>\n",
       "      <td>0.016944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.755364</td>\n",
       "      <td>0.723613</td>\n",
       "      <td>0.061937</td>\n",
       "      <td>0.025910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.743391</td>\n",
       "      <td>0.707681</td>\n",
       "      <td>0.057646</td>\n",
       "      <td>0.030408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.728777</td>\n",
       "      <td>0.707681</td>\n",
       "      <td>0.064452</td>\n",
       "      <td>0.021305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.706128</td>\n",
       "      <td>0.681223</td>\n",
       "      <td>0.061241</td>\n",
       "      <td>0.018310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.694155</td>\n",
       "      <td>0.660171</td>\n",
       "      <td>0.093390</td>\n",
       "      <td>0.018178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean_train_score  mean_cv_score  std_cv_score  std_train_score\n",
       "n_neighbors                                                                \n",
       "1                    1.000000       0.755477      0.069530         0.000000\n",
       "6                    0.831135       0.792603      0.046020         0.013433\n",
       "11                   0.819152       0.802987      0.041129         0.011336\n",
       "16                   0.801863       0.782219      0.074141         0.008735\n",
       "21                   0.777934       0.766430      0.062792         0.016944\n",
       "26                   0.755364       0.723613      0.061937         0.025910\n",
       "31                   0.743391       0.707681      0.057646         0.030408\n",
       "36                   0.728777       0.707681      0.064452         0.021305\n",
       "41                   0.706128       0.681223      0.061241         0.018310\n",
       "46                   0.694155       0.660171      0.093390         0.018178"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = results_df.set_index(\"n_neighbors\")\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df[[\"mean_train_score\", \"mean_cv_score\"]].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_n_neighbours = results_df.idxmax()[\"mean_cv_score\"]\n",
    "best_n_neighbours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's try our best model on test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.905\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=best_n_neighbours)\n",
    "knn.fit(X_train, y_train)\n",
    "print(\"Test accuracy: %0.3f\" % (knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like we got lucky with the test set here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (iClicker) Exercise 4.1 \n",
    "\n",
    "**iClicker cloud join link: https://join.iclicker.com/3DP5H**\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "1. Analogy-based models find examples from the test set that are most similar to the query example we are predicting.\n",
    "2. Euclidean distance will always have a non-negative value.\n",
    "3. With $k$-NN, setting the hyperparameter $k$ to larger values typically reduces training error. \n",
    "4. Similar to decision trees, $k$-NNs finds a small set of good features.\n",
    "5. In $k$-NN, with $k > 1$, the classification of the closest neighbour to the test example always contributes the most to the prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](img/eva-coffee.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## More on $k$-NNs [[video](https://youtu.be/IRGbqi5S9gQ)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Other useful arguments of `KNeighborsClassifier`\n",
    "\n",
    "- `weights` $\\rightarrow$ When predicting label, you can assign higher weight to the examples which are closer to the query example.  \n",
    "- Exercise for you: Play around with this argument. Do you get a better validation score? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Regression with $k$-nearest neighbours ($k$-NNs)\n",
    "\n",
    "- Can we solve regression problems with $k$-nearest neighbours algorithm? \n",
    "- In $k$-NN regression we take the average of the $k$-nearest neighbours. \n",
    "- We can also have weighted regression. \n",
    "\n",
    "See an example of regression in the lecture notes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_knn_regression(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_knn_regression(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pros of $k$-NNs for supervised learning\n",
    "\n",
    "- Easy to understand, interpret.\n",
    "- Simple hyperparameter $k$ (`n_neighbors`) controlling the fundamental tradeoff.\n",
    "- Can learn very complex functions given enough data.\n",
    "- Lazy learning: Takes no time to `fit`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Cons of $k$-NNs for supervised learning\n",
    "\n",
    "- Can be potentially be VERY slow during prediction time, especially when the training set is very large. \n",
    "- Often not that great test accuracy compared to the modern approaches.\n",
    "- It does not work well on datasets with many features or where most feature values are 0 most of the time (sparse datasets).    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Attention\n",
    ":class: important\n",
    "\n",
    "For regular $k$-NN for supervised learning (not with sparse matrices), you should scale your features. We'll be looking into it soon. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Parametric vs non parametric \n",
    "\n",
    "- You might see a lot of definitions of these terms.\n",
    "- A simple way to think about this is: \n",
    "    - do you need to store at least $O(n)$ worth of stuff to make predictions? If so, it's non-parametric.\n",
    "- Non-parametric example: $k$-NN is a classic example of non-parametric models.     \n",
    "- Parametric example: decision stump\n",
    "- If you want to know more about this, find some reading material [here](https://www.cs.ubc.ca/~schmidtm/Courses/340-F16/L6.pdf), [here](http://mlss.tuebingen.mpg.de/2015/slides/ghahramani/gp-neural-nets15.pdf), and [here](https://machinelearningmastery.com/parametric-and-nonparametric-machine-learning-algorithms/). \n",
    "- By the way, the terms \"parametric\" and \"non-paramteric\" are often used differently by statisticians, see [here](https://help.xlstat.com/s/article/what-is-the-difference-between-a-parametric-and-a-nonparametric-test?language=en_US) for more..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "$\\mathcal{O}(n)$ is referred to as big $\\mathcal{O}$ notation. It tells you how fast an algorithm is or how much storage space it requires. For example, in simple terms, if you have $n$ examples and you need to store them all you can say that the algorithm requires $\\mathcal{O}(n)$ worth of stuff. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Curse of dimensionality\n",
    "\n",
    "- Affects all learners but especially bad for nearest-neighbour. \n",
    "- $k$-NN usually works well when the number of dimensions $d$ is small but things fall apart quickly as $d$ goes up.\n",
    "- If there are many irrelevant attributes, $k$-NN is hopelessly confused because all of them contribute to finding similarity between examples. \n",
    "- With enough irrelevant attributes the accidental similarity swamps out meaningful similarity and $k$-NN is no better than random guessing.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "nfeats_accuracy = {\"nfeats\": [], \"dummy_valid_accuracy\": [], \"KNN_valid_accuracy\": []}\n",
    "for n_feats in range(4, 2000, 100):\n",
    "    X, y = make_classification(n_samples=2000, n_features=n_feats, n_classes=2)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.2, random_state=123\n",
    "    )\n",
    "    dummy = DummyClassifier(strategy=\"most_frequent\")\n",
    "    dummy_scores = cross_validate(dummy, X_train, y_train, return_train_score=True)\n",
    "\n",
    "    knn = KNeighborsClassifier()\n",
    "    scores = cross_validate(knn, X_train, y_train, return_train_score=True)\n",
    "    nfeats_accuracy[\"nfeats\"].append(n_feats)\n",
    "    nfeats_accuracy[\"KNN_valid_accuracy\"].append(np.mean(scores[\"test_score\"]))\n",
    "    nfeats_accuracy[\"dummy_valid_accuracy\"].append(np.mean(dummy_scores[\"test_score\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>nfeats</th>\n",
       "      <th>dummy_valid_accuracy</th>\n",
       "      <th>KNN_valid_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0.507500</td>\n",
       "      <td>0.950625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.723750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>204</td>\n",
       "      <td>0.501875</td>\n",
       "      <td>0.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304</td>\n",
       "      <td>0.501875</td>\n",
       "      <td>0.660625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>404</td>\n",
       "      <td>0.500625</td>\n",
       "      <td>0.650625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>504</td>\n",
       "      <td>0.502500</td>\n",
       "      <td>0.711875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>604</td>\n",
       "      <td>0.501875</td>\n",
       "      <td>0.626250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>704</td>\n",
       "      <td>0.504375</td>\n",
       "      <td>0.605000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>804</td>\n",
       "      <td>0.501875</td>\n",
       "      <td>0.617500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>904</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.648750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1004</td>\n",
       "      <td>0.501250</td>\n",
       "      <td>0.549375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1104</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.578750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1204</td>\n",
       "      <td>0.508750</td>\n",
       "      <td>0.632500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1304</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.579375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1404</td>\n",
       "      <td>0.501250</td>\n",
       "      <td>0.578125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1504</td>\n",
       "      <td>0.506250</td>\n",
       "      <td>0.585625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1604</td>\n",
       "      <td>0.503125</td>\n",
       "      <td>0.613750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1704</td>\n",
       "      <td>0.501875</td>\n",
       "      <td>0.591250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1804</td>\n",
       "      <td>0.503750</td>\n",
       "      <td>0.579375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1904</td>\n",
       "      <td>0.502500</td>\n",
       "      <td>0.576250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nfeats  dummy_valid_accuracy  KNN_valid_accuracy\n",
       "0        4              0.507500            0.950625\n",
       "1      104              0.510000            0.723750\n",
       "2      204              0.501875            0.775000\n",
       "3      304              0.501875            0.660625\n",
       "4      404              0.500625            0.650625\n",
       "5      504              0.502500            0.711875\n",
       "6      604              0.501875            0.626250\n",
       "7      704              0.504375            0.605000\n",
       "8      804              0.501875            0.617500\n",
       "9      904              0.500000            0.648750\n",
       "10    1004              0.501250            0.549375\n",
       "11    1104              0.510000            0.578750\n",
       "12    1204              0.508750            0.632500\n",
       "13    1304              0.510000            0.579375\n",
       "14    1404              0.501250            0.578125\n",
       "15    1504              0.506250            0.585625\n",
       "16    1604              0.503125            0.613750\n",
       "17    1704              0.501875            0.591250\n",
       "18    1804              0.503750            0.579375\n",
       "19    1904              0.502500            0.576250"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(nfeats_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Support Vector Machines (SVMs) with RBF kernel [[video](https://youtu.be/ic_zqOhi020)]\n",
    "\n",
    "- Very high-level overview\n",
    "- Our goals here are\n",
    "    - Use `scikit-learn`'s SVM model. \n",
    "    - Broadly explain the notion of support vectors.  \n",
    "    - Broadly explain the similarities and differences between $k$-NNs and SVM RBFs.\n",
    "    - Explain how `C` and `gamma` hyperparameters control the fundamental tradeoff.\n",
    "    \n",
    "> (Optional) RBF stands for radial basis functions. We won't go into what it means in this video. Refer to [this video](https://www.youtube.com/watch?v=Qc5IyLW_hns) if you want to know more. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Overview\n",
    "\n",
    "- Another popular similarity-based algorithm is Support Vector Machines with RBF Kernel (SVM RBFs)\n",
    "- Superficially, SVM RBFs are more like weighted $k$-NNs.\n",
    "    - The decision boundary is defined by **a set of positive and negative examples** and **their weights** together with **their similarity measure**. \n",
    "    - A test example is labeled positive if on average it looks more like positive examples than the negative examples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The primary difference between $k$-NNs and SVM RBFs is that \n",
    "    - Unlike $k$-NNs, SVM RBFs only remember the key examples (support vectors). \n",
    "    - SVMs use a different similarity metric which is called a \"kernel\". A popular kernel is Radial Basis Functions (RBFs)\n",
    "    - They usually perform better than $k$-NNs! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's explore SVM RBFs\n",
    "\n",
    "Let's try SVMs on the cities dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.discrete_scatter(X_cities.iloc[:, 0], X_cities.iloc[:, 1], y_cities)\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"latitude\")\n",
    "plt.legend(loc=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_cities, y_cities, test_size=0.2, random_state=123\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean validation score 0.803\n"
     ]
    },
    {
     "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000843</td>\n",
       "      <td>0.002528</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.819549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.000822</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.819549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000514</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.850746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.828358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.783582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.000843    0.002528    0.794118     0.819549\n",
       "1  0.000595    0.000822    0.764706     0.819549\n",
       "2  0.000514    0.000773    0.727273     0.850746\n",
       "3  0.000515    0.000767    0.787879     0.828358\n",
       "4  0.000499    0.000771    0.939394     0.783582"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=best_n_neighbours)\n",
    "scores = cross_validate(knn, X_train, y_train, return_train_score=True)\n",
    "print(\"Mean validation score %0.3f\" % (np.mean(scores[\"test_score\"])))\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean validation score 0.820\n"
     ]
    },
    {
     "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002057</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.858209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000692</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.843284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000701</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.805970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.002057    0.001008    0.823529     0.842105\n",
       "1  0.000745    0.000449    0.823529     0.842105\n",
       "2  0.000678    0.000420    0.727273     0.858209\n",
       "3  0.000692    0.000480    0.787879     0.843284\n",
       "4  0.000701    0.000432    0.939394     0.805970"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(gamma=0.01)  # Ignore gamma for now\n",
    "scores = cross_validate(svm, X_train, y_train, return_train_score=True)\n",
    "print(\"Mean validation score %0.3f\" % (np.mean(scores[\"test_score\"])))\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Decision boundary of SVMs \n",
    "- We can think of SVM with RBF kernel as \"smooth KNN\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n",
    "\n",
    "for clf, ax in zip([knn, svm], axes):\n",
    "    clf.fit(X_train, y_train)\n",
    "    mglearn.plots.plot_2d_separator(\n",
    "        clf, X_train.to_numpy(), fill=True, eps=0.5, ax=ax, alpha=0.4\n",
    "    )\n",
    "    mglearn.discrete_scatter(X_train.iloc[:, 0], X_train.iloc[:, 1], y_train, ax=ax)\n",
    "    ax.set_title(clf)\n",
    "    ax.set_xlabel(\"longitude\")\n",
    "    ax.set_ylabel(\"latitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Support vectors \n",
    "\n",
    "- Each training example either is or isn't a \"support vector\".\n",
    "  - This gets decided during `fit`.\n",
    "\n",
    "- **Main insight: the decision boundary only depends on the support vectors.**\n",
    "\n",
    "- Let's look at the support vectors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "n = 20\n",
    "n_classes = 2\n",
    "X_toy, y_toy = make_blobs(\n",
    "    n_samples=n, centers=n_classes, random_state=300\n",
    ")  # Let's generate some fake data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.discrete_scatter(X_toy[:, 0], X_toy[:, 1], y_toy)\n",
    "plt.xlabel(\"Feature 0\")\n",
    "plt.ylabel(\"Feature 1\")\n",
    "svm = SVC(kernel=\"rbf\", C=10, gamma=0.1).fit(X_toy, y_toy)\n",
    "mglearn.plots.plot_2d_separator(svm, X_toy, fill=True, eps=0.5, alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  8,  9, 14, 19,  1,  4,  6, 17], dtype=int32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_support_vectors(svm, X_toy, y_toy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The support vectors are the bigger points in the plot above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Hyperparameters of SVM \n",
    "\n",
    "- Key hyperparameters of `rbf` SVM are\n",
    "    - `gamma`\n",
    "    - `C`\n",
    "    \n",
    "- We are not equipped to understand the meaning of these parameters at this point but you are expected to describe their relation to the fundamental tradeoff. \n",
    "\n",
    "See [`scikit-learn`'s explanation of RBF SVM parameters](https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Relation of `gamma` and the fundamental trade-off\n",
    "\n",
    "- `gamma` controls the complexity (fundamental trade-off), just like other hyperparameters we've seen.\n",
    "  - larger `gamma` $\\rightarrow$ more complex\n",
    "  - smaller `gamma` $\\rightarrow$ less complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pGgJkL4YUgBR0/Phx/ehHP9KOHTvU3t6unp4eFRYWqqKiQl/4whf05S9/mSgDAEKiIQAAp2gIAMAJ+gEAcIqGANmLIQUAAAAAAAAAAAAAAJAQOck+AAAAAAAAAAAAAAAAkB0YUgAAAAAAAAAAAAAAAAnBkAIAAAAAAAAAAAAAAEgIhhQAAAAAAAAAAAAAAEBCMKQAAAAAAAAAAAAAAAASgiEFAAAAAAAAAAAAAACQEAwpAAAAAAAAAAAAAACAhGBIAQAAAAAAAAAAAAAAJMT/B1V2+RX1G2yvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2500x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma = [0.001, 0.01, 0.1, 1.0, 10.0]\n",
    "plot_svc_gamma(\n",
    "    gamma,\n",
    "    X_train.to_numpy(),\n",
    "    y_train.to_numpy(),\n",
    "    x_label=\"longitude\",\n",
    "    y_label=\"latitude\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Relation of `C` and the fundamental trade-off\n",
    "\n",
    "- `C` _also_ affects the fundamental tradeoff\n",
    "    - larger `C` $\\rightarrow$ more complex \n",
    "    - smaller `C` $\\rightarrow$ less complex "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = [0.1, 1.0, 100.0, 1000.0, 100000.0]\n",
    "plot_svc_C(\n",
    "    C, X_train.to_numpy(), y_train.to_numpy(), x_label=\"longitude\", y_label=\"latitude\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Search over multiple hyperparameters\n",
    "\n",
    "- So far you have seen how to carry out search over a hyperparameter\n",
    "- In the above case the best training error is achieved by the most complex model (large `gamma`, large `C`).\n",
    "- Best validation error requires a hyperparameter search to balance the fundamental tradeoff.\n",
    "  - In general we can't search them one at a time.\n",
    "  - More on this next week. But if you cannot wait till then, you may look up the following:\n",
    "    - [sklearn.model_selection.GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)\n",
    "    - [sklearn.model_selection.RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### SVM Regressor\n",
    "\n",
    "- Similar to KNNs, you can use SVMs for regression problems as well.\n",
    "- See [`sklearn.svm.SVR`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html) for more details. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "source": [
    "## (iClicker) Exercise 4.2 \n",
    "\n",
    "**iClicker cloud join link: https://join.iclicker.com/3DP5H**\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "1. $k$-NN may perform poorly in high-dimensional space (say, *d* > 1000). \n",
    "2. In SVM RBF, removing a non-support vector would not change the decision boundary. \n",
    "3. In sklearn’s SVC classifier, large values of gamma tend to result in higher training score but probably lower validation score. \n",
    "4. If we increase both gamma and C, we can't be certain if the model becomes more complex or less complex."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### More practice questions \n",
    "\n",
    "- Check out some more practice questions [here](https://ml-learn.mds.ubc.ca/en/module4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Summary\n",
    "\n",
    "- We have KNNs and SVMs as new supervised learning techniques in our toolbox.\n",
    "- These are analogy-based learners and the idea is to assign nearby points the same label.\n",
    "- Unlike decision trees, all features are equally important. \n",
    "- Both can be used for classification or regression (much like the other methods we've seen)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Coming up:\n",
    "\n",
    "Lingering questions: \n",
    "- Are we ready to do machine learning on real-world datasets?\n",
    "- What would happen if we use $k$-NNs or SVM RBFs on the spotify dataset from hw1?  \n",
    "- What happens if we have missing values in our data? \n",
    "- What do we do if we have features with categories or string values?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "![](img/eva-seeyou.png)"
   ]
  }
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