{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "df630e55-57dd-470a-849b-f1bc90ac719e"
    }
   },
   "source": [
    "# 1. Importing and Visualization of CIFAR-10 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "nbpresent": {
     "id": "409a1ab7-fe1d-4430-b904-7694020a6223"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# function to import CIFAR-10 data set\n",
    "def unpickle(file):\n",
    "    import pickle\n",
    "    with open(file, 'rb') as fo:\n",
    "        dict = pickle.load(fo, encoding='bytes')\n",
    "    return dict\n",
    "data_batch_1 = unpickle(\"./data/data_batch_1\")\n",
    "data_batch_2 = unpickle(\"./data/data_batch_2\")\n",
    "data_batch_3 = unpickle(\"./data/data_batch_3\")\n",
    "data_batch_4 = unpickle(\"./data/data_batch_4\")\n",
    "data_batch_5 = unpickle(\"./data/data_batch_5\")\n",
    "test_batch = unpickle(\"./data/test_batch\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ce2be501-0be3-4750-8207-dfc00d7db01a"
    }
   },
   "source": [
    "What is the data structure of e.g. data_batch_1 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "nbpresent": {
     "id": "f77bd9ec-de3b-4c56-b08d-4a65f0780408"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_batch_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "09a5b60b-dcbb-4f97-ab57-e4611c253e2e"
    }
   },
   "source": [
    "What are the keys of e.g. data_batch_1 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "nbpresent": {
     "id": "c874a7c9-de0c-4ccd-a0f1-8f8a3265a0b6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'batch_label', b'labels', b'data', b'filenames'])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_batch_1.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a7f910e7-0b11-453b-84d5-df6ac88ac6dd"
    }
   },
   "source": [
    "What is the data structure of data_batch_1[b'data'] ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "nbpresent": {
     "id": "fe299a35-c930-4078-97b7-c9b67f42ec42"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_batch_1[b'data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3f978c4f-50d0-4f00-9f19-bf8744e505a3"
    }
   },
   "source": [
    "What is the data structure of data_batch_1[b'labels'] ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbpresent": {
     "id": "46a97575-36c0-4920-a8dc-762e94239b7e"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_batch_1[b'labels'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "2fd19982-c318-4303-8042-7a5a6998d175"
    }
   },
   "source": [
    "What is the shape of data_batch_1[b'data'] ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "nbpresent": {
     "id": "b012720d-81f8-455d-8ce7-bfca64a842c8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 3072)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_batch_1[b'data'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "378aeda2-a547-435e-b28b-09ceb0074a53"
    }
   },
   "source": [
    "What is the size of data_batch_1[b'labels'] ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "49c776cb-c8aa-461b-a0da-4f4d38342e2e"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_batch_1[b'labels'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "02ca495c-e6d2-48d4-9bc2-2295272d5f6f"
    }
   },
   "source": [
    "What are the first 10 elements of data_batch_1[b'labels'] ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbpresent": {
     "id": "438920f4-774e-4e94-9b7c-30a2106d163c"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 9, 9, 4, 1, 1, 2, 7, 8, 3]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_batch_1[b'labels'][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "1e599fcd-a46c-4750-94f8-1cf4ad8fb342"
    }
   },
   "source": [
    "What is the data type of data_batch_1[b'data'] ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbpresent": {
     "id": "7617a699-c3d5-434f-97a5-3443489ac9db"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_batch_1[b'data'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "067d850d-0411-4af6-8714-a79b310ca8c1"
    }
   },
   "source": [
    "Let us concatenate the batch training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbpresent": {
     "id": "942f351b-b771-4375-8df2-eec28391a576"
    }
   },
   "outputs": [],
   "source": [
    "X_train=np.concatenate([data_batch_1[b'data'], \n",
    "                         data_batch_2[b'data'], \n",
    "                         data_batch_3[b'data'], \n",
    "                         data_batch_4[b'data'], \n",
    "                         data_batch_5[b'data']], \n",
    "                         axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b289f0b9-b3ab-480b-9ae6-76a893980efe"
    }
   },
   "source": [
    "Let us concatenate the training labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "nbpresent": {
     "id": "9b85b9a0-5f2b-4c68-a74f-82f1ec212215"
    }
   },
   "outputs": [],
   "source": [
    "y_train=np.concatenate([data_batch_1[b'labels'] , \n",
    "                data_batch_2[b'labels'],\n",
    "                data_batch_3[b'labels'],\n",
    "                data_batch_4[b'labels'],\n",
    "                data_batch_5[b'labels']], \n",
    "                axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d9967582-1305-4b95-948b-e75c46fc49bb"
    }
   },
   "source": [
    "Let us define the test data as X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbpresent": {
     "id": "5c85918c-f89e-4156-8cdd-ca38d14afbb9"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 3072)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = test_batch[b'data']\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "48754d72-9acd-49cf-b209-737c45047284"
    }
   },
   "source": [
    "Let us cast the test labels as ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "nbpresent": {
     "id": "5f913d95-aa49-4727-8c6f-5630cbf59741"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test=np.array(test_batch[b'labels']) \n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f4c3aa97-0d97-4e9c-a6b3-f2d2b1f08632"
    }
   },
   "source": [
    "What is the shape of X_train ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nbpresent": {
     "id": "a0eb7a33-19c9-46e4-b471-6f7904389177"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 3072)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "81d3c33f-544e-44c8-8260-e89143cc6ef1"
    }
   },
   "source": [
    "What is the shape of Y_train ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbpresent": {
     "id": "d699e7a7-efc0-421f-bd8d-2d2b34a09516"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d0a61e44-a2a9-4dff-9849-5f6a0e232290"
    }
   },
   "source": [
    "Let us visualize an image. \n",
    "\n",
    "By default, NumPy arrays are created in row major order. \n",
    "Spatially this means, that if we have a two-dimensional \n",
    "array of data, the items in each row of the array are stored \n",
    "in adjacent memory locations. In the case of a three-dimensional \n",
    "array of data, the items along `axis=2` are stored in adjacent order. \n",
    "\n",
    "Since the first $32$ entries of the array $X\\_train[0]$ are the \n",
    "red channel values of the first row of the image, etc., we need to \n",
    "pass the tuple $(3,32,32)$ to reshape. \n",
    "\n",
    "By default, NumPy arrays are created in row major order, that is, when \n",
    "reshaping the array, higher order dimensions are traversed first \n",
    "(e.g. `axis=2`  before advancing on `axis=1`.)\n",
    "\n",
    "\n",
    "`plt.imshow` needs for each inner list the values representing \n",
    "a pixel. Here, with an RGB image, there are 3 values. We thus \n",
    "need to transpose the array : the RGB values need to be located \n",
    "along `axis=2`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "nbpresent": {
     "id": "d817d603-7d37-4ff2-b3d1-e95875b48f8f"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(X_train[20].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "38cf20c0-9404-4f32-91ed-a00e910832f8"
    }
   },
   "source": [
    "We visualize some examples from the dataset.\n",
    "We show a few examples of training images from each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbpresent": {
     "id": "ba3743b9-ea50-4201-ad99-5fa47e8b82fb"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "\n",
    "\n",
    "\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ab168c02-9867-455d-815d-c2de707e2f87"
    }
   },
   "source": [
    "# 2. K-Nearest-Neighbour Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "36557d31-2ba4-416c-8ead-a92fb7446e85"
    }
   },
   "source": [
    " We subsample the data for more efficient code execution in this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "nbpresent": {
     "id": "26316896-3b01-455b-9a0a-87278f088d83"
    }
   },
   "outputs": [],
   "source": [
    "num_training = 5000\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "27db2b6f-c417-4d15-bff4-8c00d58cb808"
    }
   },
   "source": [
    "We define Class KNearestNeighbor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "nbpresent": {
     "id": "497fbf77-9a17-4b35-a0d8-375972850902"
    }
   },
   "outputs": [],
   "source": [
    "class KNearestNeighbor():\n",
    "  \"\"\" a kNN classifier with L2 distance \"\"\"\n",
    "\n",
    "  def __init__(self):\n",
    "    pass\n",
    "\n",
    "  def train(self, X, y):\n",
    "    \"\"\"\n",
    "    Train the classifier. For k-nearest neighbors this is just \n",
    "    memorizing the training data.\n",
    "\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_train, D) containing the training data\n",
    "      consisting of num_train samples each of dimension D.\n",
    "    - y: A numpy array of shape (N,) containing the training labels, where\n",
    "         y[i] is the label for X[i].\n",
    "    \"\"\"\n",
    "    self.X_train = X.astype('float')\n",
    "    self.y_train = y\n",
    "    \n",
    "  def predict(self, X, k=1, num_loops=0):\n",
    "    \"\"\"\n",
    "    Predict labels for test data using this classifier.\n",
    "\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_test, D) containing test data consisting\n",
    "         of num_test samples each of dimension D.\n",
    "    - k: The number of nearest neighbors that vote for the predicted labels.\n",
    "    - num_loops: Determines which implementation to use to compute distances\n",
    "      between training points and testing points.\n",
    "\n",
    "    Returns:\n",
    "    - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
    "      test data, where y[i] is the predicted label for the test point X[i].  \n",
    "    \"\"\"\n",
    "    if num_loops == 0:\n",
    "      dists = self.compute_distances_no_loops(X)\n",
    "    elif num_loops == 1:\n",
    "      dists = self.compute_distances_one_loop(X)\n",
    "    elif num_loops == 2:\n",
    "      dists = self.compute_distances_two_loops(X)\n",
    "    else:\n",
    "      raise ValueError('Invalid value %d for num_loops' % num_loops)\n",
    "\n",
    "    return self.predict_labels(dists, k=k)\n",
    "\n",
    "  def compute_distances_two_loops(self, X):\n",
    "    \"\"\"\n",
    "    Compute the distance between each test point in X and each \n",
    "    training point in self.X_train using a nested loop over both \n",
    "    the training data and the test data.\n",
    "\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_test, D) containing test data.\n",
    "\n",
    "    Returns:\n",
    "    - dists: A numpy array of shape (num_test, num_train) where \n",
    "      dists[i, j] is the Euclidean distance between the ith test \n",
    "      point and the jth training point.\n",
    "    \"\"\"\n",
    "    num_test = X.shape[0]\n",
    "    num_train = self.X_train.shape[0]\n",
    "    dists = np.zeros((num_test, num_train))\n",
    "    X = X.astype('float')\n",
    "    for i in range(num_test):\n",
    "      for j in range(num_train):\n",
    "          dists[i, j] = np.sqrt(np.sum(np.square(self.X_train[j,:] - X[i,:])))\n",
    "        \n",
    "    return dists\n",
    "\n",
    "  def compute_distances_one_loop(self, X):\n",
    "    \"\"\"\n",
    "    Compute the distance between each test point in X and each training point\n",
    "    in self.X_train using a single loop over the test data.\n",
    "\n",
    "    Input / Output: Same as compute_distances_two_loops\n",
    "    \"\"\"\n",
    "    num_test = X.shape[0]\n",
    "    num_train = self.X_train.shape[0]\n",
    "    dists = np.zeros((num_test, num_train))\n",
    "    X = X.astype('float')\n",
    "    for i in range(num_test):\n",
    "      dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i,:]), axis = 1))\n",
    "      \n",
    "     \n",
    "    return dists\n",
    "\n",
    "  def compute_distances_no_loops(self, X):\n",
    "    \"\"\"\n",
    "    Compute the distance between each test point in X and each training point\n",
    "    in self.X_train using no explicit loops.\n",
    "\n",
    "    Input / Output: Same as compute_distances_two_loops\n",
    "    \"\"\"\n",
    "    num_test = X.shape[0]\n",
    "    num_train = self.X_train.shape[0]\n",
    "    dists = np.zeros((num_test, num_train)) \n",
    "    X=X.astype('float')\n",
    "    \n",
    "    # Most \"elegant\" solution leads however to memory issues\n",
    "    # dists = np.sqrt(np.square((self.X_train[:, np.newaxis, :] - X)).sum(axis=2)).T\n",
    "    # split (p-q)^2 to p^2 + q^2 - 2pq\n",
    "    dists = np.sqrt((X**2).sum(axis=1)[:, np.newaxis] + (self.X_train**2).sum(axis=1) - 2 * X.dot(self.X_train.T))\n",
    "                     \n",
    "    \n",
    "    \n",
    "    return dists\n",
    "\n",
    "  def predict_labels(self, dists, k=1):\n",
    "    \"\"\"\n",
    "    Given a matrix of distances between test points and training points,\n",
    "    predict a label for each test point.\n",
    "\n",
    "    Inputs:\n",
    "    - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n",
    "      gives the distance betwen the ith test point and the jth training point.\n",
    "\n",
    "    Returns:\n",
    "    - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
    "      test data, where y[i] is the predicted label for the test point X[i].  \n",
    "    \"\"\"\n",
    "    num_test = dists.shape[0]\n",
    "    y_pred = np.zeros(num_test, dtype='float64')\n",
    "    for i in range(num_test):\n",
    "        # A list of length k storing the labels of the k nearest neighbors to\n",
    "        # the ith test point.\n",
    "        closest_y = []\n",
    "        # get the k indices with smallest distances\n",
    "        min_indices = np.argsort(dists[i,:])[:k] \n",
    "        closest_y = np.bincount(self.y_train[min_indices])\n",
    "        # predict the label of the nearest example\n",
    "        y_pred[i] = np.argmax(closest_y)  \n",
    "\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "91c8998c-f531-4774-98ca-6c9631050fd3"
    }
   },
   "source": [
    "Create an instance nn from the class KNearestNeighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "nbpresent": {
     "id": "215be79c-8fe0-4e10-9587-6bea172bb33a"
    }
   },
   "outputs": [],
   "source": [
    "classifier = KNearestNeighbor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "2f886096-8250-4739-8645-37950f408d41"
    }
   },
   "source": [
    "We call the method `train` of the `KNearestNeighbor` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "nbpresent": {
     "id": "de24c3a8-0860-446e-b974-3e0c334feced"
    }
   },
   "outputs": [],
   "source": [
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d058a8de-3c50-4514-8405-5aff67b26398"
    }
   },
   "source": [
    "We test our implementation with two_loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "nbpresent": {
     "id": "d87bb3a8-6338-4957-ac73-4c81b87821eb"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 5000)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "dists.shape  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c1277b26-a267-4dec-ab9d-e44d31cdaa3e"
    }
   },
   "source": [
    "We can visualize the distance matrix: each row is a single test example and its distances to training examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "nbpresent": {
     "id": "ae3a05a2-a3e6-4e65-a59f-0204411f57f9"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7855beeb-d7e2-4ea7-994f-1adfa5a2c886"
    }
   },
   "source": [
    "Let us now predict labels and run the code below: We use $k = 1$ (which is Nearest Neighbor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "nbpresent": {
     "id": "219d7522-e633-4136-aa98-9abe80ca7bf3"
    }
   },
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f083926f-4bd0-488f-8ba9-e77dc946fac8"
    }
   },
   "source": [
    "We compute and print the fraction of correctly predicted examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "nbpresent": {
     "id": "f1ac90b4-5005-4940-9663-0bfd9574dc8c"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7a33b48c-c106-4903-ba68-769ce91ccb8b"
    }
   },
   "source": [
    " Let us now predict labels and run the code below: We use k = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "nbpresent": {
     "id": "7a4433f3-d7d4-4b7c-bd21-6f6d5272c837"
    }
   },
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8cede653-c157-4396-a534-b4a8741251e2"
    }
   },
   "source": [
    "We compute and print the fraction of correctly predicted examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "nbpresent": {
     "id": "445220c9-4974-41a0-a36c-a309d395490b"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / len(y_test_pred)\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# utility function for plotting confusion matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def plot_confmat(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Plot the confusion matrix and save to user_files dir\n",
    "    \"\"\"\n",
    "    conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "    fig = plt.figure(figsize=(9,9))\n",
    "    ax = fig.add_subplot(111)\n",
    "    sns.heatmap(conf_matrix,\n",
    "                annot=True,\n",
    "                fmt='.0f')\n",
    "    plt.title('Confusion matrix')\n",
    "    ax.set_xticklabels( classes)\n",
    "    ax.set_yticklabels( classes)\n",
    "    plt.ylabel('True')\n",
    "    plt.xlabel('Predicted')\n",
    "    \n",
    "plot_confmat(y_test, y_test_pred)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "df615e0b-aeeb-4074-abef-075af4118640"
    }
   },
   "source": [
    "## Algebra and Performance of Distance Matrix Computation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "04f92811-3067-4a08-8227-ed55c42fed50"
    }
   },
   "source": [
    "To ensure that our vectorized implementation is correct, we make sure that it\n",
    "agrees with the naive implementation. There are many ways to decide whether\n",
    "two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "root of the squared sum of differences of all elements; in other words, reshape\n",
    "the matrices into vectors and compute the Euclidean distance between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "nbpresent": {
     "id": "edecc2dc-bbf4-47bb-8902-6910fef3eae0"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n",
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "dists_two  = classifier.compute_distances_two_loops(X_test)\n",
    "dists_one  = classifier.compute_distances_one_loop(X_test)\n",
    "dists_zero = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "\n",
    "difference_two_2_one = np.linalg.norm(dists_two - dists_one, ord='fro')\n",
    "print('Difference was: %f' % (difference_two_2_one, ))\n",
    "if difference_two_2_one < 0.001:\n",
    "  print('Good! The distance matrices are the same')\n",
    "else:\n",
    "  print('Uh-oh! The distance matrices are different')\n",
    "\n",
    "difference_one_2_zero = np.linalg.norm(dists_one - dists_zero, ord='fro')\n",
    "print('Difference was: %f' % (difference_one_2_zero, ))\n",
    "if difference_one_2_zero < 0.001:\n",
    "  print('Good! The distance matrices are the same')\n",
    "else:\n",
    "  print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "94c6dacb-929f-4378-b80f-4859256bd7f4"
    }
   },
   "source": [
    "Let's compare how fast the implementations are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "nbpresent": {
     "id": "1d3c6b0c-9a33-4f71-b283-0b1eb8061e77"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 66.663865 seconds\n",
      "One loop version took 81.752124 seconds\n",
      "No loop version took 0.792757 seconds\n"
     ]
    }
   ],
   "source": [
    "def time_function(f, *args):\n",
    "  \"\"\"\n",
    "  Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "  \"\"\"\n",
    "  import time\n",
    "  tic = time.time()\n",
    "  f(*args)\n",
    "  toc = time.time()\n",
    "  return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e55a0c49-3d30-47b3-bbfc-2ba53025a0eb"
    }
   },
   "source": [
    "#  3. k-fold cross validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "nbpresent": {
     "id": "48a7d639-21bd-4b58-892d-c54a818111aa"
    }
   },
   "outputs": [],
   "source": [
    "num_folds = 5\n",
    "\n",
    "k_choices = [1, 3, 5, 7, 9, 10, 12, 15, 18, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8b1aa44f-7099-4511-8b20-168c0f37edb9"
    }
   },
   "source": [
    "Split up the training data into folds. After splitting, `X_train_folds` and    \n",
    "`y_train_folds` should each be lists of length `num_folds`, where                \n",
    "`y_train_folds[i]` is the label vector for the points in `X_train_folds[i]`.     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "nbpresent": {
     "id": "ee7f2e26-fa37-45b0-af4c-c225369eedc2"
    }
   },
   "outputs": [],
   "source": [
    "num_train = X_train.shape[0]\n",
    "fold_size = np.ceil(num_train/num_folds).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "235c4927-a8f9-475f-83c4-54fb4b1de699"
    }
   },
   "source": [
    "In the case of `num_train = 5000` and 5 folds, we obtain \n",
    "`X_train_folds = np.split(X_train, [1000, 2000, 3000, 4000])`\n",
    "`y_train_folds = np.split(y_train, [1000, 2000, 3000, 4000])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "nbpresent": {
     "id": "dd9d3e91-fb0d-4ea1-8e37-6282e1eea5f5"
    }
   },
   "outputs": [],
   "source": [
    "X_train_folds = np.split(X_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n",
    "y_train_folds = np.split(y_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3072)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_folds[1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "99d20b22-bc30-49c6-85a1-86f153b21fe0"
    }
   },
   "source": [
    "A dictionary holding the accuracies for different values of $k$ that we find\n",
    "when running cross-validation. After running cross-validation,\n",
    "`k_to_accuracies[k]` should be a list of length `num_folds` giving the different\n",
    "accuracy values that we found when using that value of $k$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "nbpresent": {
     "id": "a14b3164-b63a-49eb-980e-57c74b2304db"
    }
   },
   "outputs": [],
   "source": [
    "k_to_accuracies = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "369cc408-fb92-4899-9e37-92c02a9ef4c1"
    }
   },
   "source": [
    "We perform $k$-fold cross validation to find the best value of $k$. For each     \n",
    "possible value of $k$, run the $k$-nearest-neighbor algorithm `num_folds` times,   \n",
    "where in each case you use all but one of the folds as training data and the \n",
    "last fold as a validation set. Store the accuracies for all fold and all     \n",
    "values of k in the `k_to_accuracies` dictionary.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "nbpresent": {
     "id": "6c869757-5e74-48cc-b7ef-14246b832a99"
    }
   },
   "outputs": [],
   "source": [
    "for k in k_choices:\n",
    "  \n",
    "  k_to_accuracies[k] = []\n",
    "  classifier = KNearestNeighbor()\n",
    "  for i in range(num_folds):\n",
    "      X_cv_training = np.concatenate([x for k, x in enumerate(X_train_folds) if k!=i], axis=0)\n",
    "      y_cv_training = np.concatenate([x for k, x in enumerate(y_train_folds) if k!=i], axis=0)\n",
    "      classifier.train(X_cv_training, y_cv_training)\n",
    "      dists = classifier.compute_distances_no_loops(X_train_folds[i])\n",
    "      y_test_pred = classifier.predict_labels(dists, k=k)\n",
    "      k_to_accuracies[k].append(np.mean(y_train_folds[i] == y_test_pred))\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c10d6b24-607c-470b-bffd-614c8fa0be2c"
    }
   },
   "source": [
    "We print out the computed accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "nbpresent": {
     "id": "d7c42393-850e-4329-91db-5c052fe247e0"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.239000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.240000\n",
      "k = 3, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.254000\n",
      "k = 5, accuracy = 0.248000\n",
      "k = 5, accuracy = 0.266000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.292000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 7, accuracy = 0.261000\n",
      "k = 7, accuracy = 0.279000\n",
      "k = 7, accuracy = 0.268000\n",
      "k = 7, accuracy = 0.288000\n",
      "k = 7, accuracy = 0.276000\n",
      "k = 9, accuracy = 0.259000\n",
      "k = 9, accuracy = 0.283000\n",
      "k = 9, accuracy = 0.270000\n",
      "k = 9, accuracy = 0.285000\n",
      "k = 9, accuracy = 0.285000\n",
      "k = 10, accuracy = 0.265000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.276000\n",
      "k = 10, accuracy = 0.284000\n",
      "k = 10, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.260000\n",
      "k = 12, accuracy = 0.295000\n",
      "k = 12, accuracy = 0.279000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.252000\n",
      "k = 15, accuracy = 0.289000\n",
      "k = 15, accuracy = 0.278000\n",
      "k = 15, accuracy = 0.282000\n",
      "k = 15, accuracy = 0.274000\n",
      "k = 18, accuracy = 0.266000\n",
      "k = 18, accuracy = 0.275000\n",
      "k = 18, accuracy = 0.281000\n",
      "k = 18, accuracy = 0.284000\n",
      "k = 18, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.285000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the raw observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "nbpresent": {
     "id": "e81573f1-9d05-44e2-a581-ffa01100b7af"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in k_choices:\n",
    "  accuracies = k_to_accuracies[k]\n",
    "  plt.scatter([k] * len(accuracies), accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "21f79bed-12f0-4e15-abdd-1105b4467cf0"
    }
   },
   "source": [
    " We plot the trend line with error bars that correspond to standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "nbpresent": {
     "id": "c9af79e8-2cfa-42ed-84fe-efbdadcf65fd"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "301c698f-4817-4bc5-8e35-ee37caebacba"
    }
   },
   "source": [
    " # K-Nearest Neighbor with L1 distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "nbpresent": {
     "id": "ce60718f-a584-4026-8071-292b5943eca4"
    }
   },
   "outputs": [],
   "source": [
    "class KNearestNeighbor_L1(KNearestNeighbor):\n",
    "  \"\"\" a kNN classifier with L1 distance \"\"\"\n",
    "\n",
    "  def __init__(self):\n",
    "    super().__init__()\n",
    "    \n",
    "\n",
    "  def compute_distances_one_loop(self, X):\n",
    "    \"\"\"\n",
    "    We overwrite the compute_distance_one_loop method of the parent class \n",
    "    KNearestNeighbor. \n",
    "    Compute the distance between each test point in X and each training point\n",
    "    in self.X_train using one loop and the L1 distance measure.\n",
    "\n",
    "    Input / Output: Same as compute_distances_two_loops\n",
    "    \"\"\"\n",
    "    num_test = X.shape[0]\n",
    "    num_train = self.X_train.shape[0]\n",
    "    dists = np.zeros((num_test, num_train))\n",
    "    X = X.astype('float')\n",
    "    for i in range(num_test):\n",
    "      dists[i, :] = (np.sum(np.abs(self.X_train - X[i,:]), axis = 1))\n",
    "    return dists \n",
    "  \n",
    "  def compute_distances_two_loops(self, X):\n",
    "    \"\"\"\n",
    "    Compute the distance between each test point in X and each \n",
    "    training point in self.X_train using a nested loop over both \n",
    "    the training data and the test data.\n",
    "\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_test, D) containing test data.\n",
    "\n",
    "    Returns:\n",
    "    - dists: A numpy array of shape (num_test, num_train) where \n",
    "      dists[i, j] is the Euclidean distance between the ith test \n",
    "      point and the jth training point.\n",
    "    \"\"\"\n",
    "    num_test = X.shape[0]\n",
    "    num_train = self.X_train.shape[0]\n",
    "    dists = np.zeros((num_test, num_train))\n",
    "    X = X.astype('float')\n",
    "    for i in range(num_test):\n",
    "      for j in range(num_train):\n",
    "          dists[i, j] = np.sum(np.abs(self.X_train[j,:] - X[i,:]))\n",
    "        \n",
    "        \n",
    "     \n",
    "    return dists\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d5745a61-1071-4704-8b71-6c0d175de9fc"
    }
   },
   "source": [
    "We create an instance nn form the class `KNearestNeighbor_L1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "nbpresent": {
     "id": "235c3d13-a428-4dae-a286-6ea912f8a0b2"
    }
   },
   "outputs": [],
   "source": [
    "classifier = KNearestNeighbor_L1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "94df5594-5eff-4354-bc83-889aca850336"
    }
   },
   "source": [
    "Call the method train of the `KNearestNeighbor` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "nbpresent": {
     "id": "627b4ca8-b0df-473d-8e53-3bcc2e31acd8"
    }
   },
   "outputs": [],
   "source": [
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b96d32ad-0526-4a52-a91e-dffb4a9e634a"
    }
   },
   "source": [
    "We test our implementation with one loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "nbpresent": {
     "id": "f6ecd69e-e8b4-44a5-8ec1-8aeb47fbc5b5"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 5000)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dists = classifier.compute_distances_one_loop(X_test)\n",
    "dists.shape  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "cd4c75ed-d9f1-4f3f-8990-2259f4f2f0d5"
    }
   },
   "source": [
    " Let us now predict labels and run the code below: We use $k = 10$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "nbpresent": {
     "id": "606e2720-6672-45f3-ae46-761df5c2066d"
    }
   },
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3408b28c-0781-4186-b1cc-f8d0040ecf8f"
    }
   },
   "source": [
    "We compute and print the fraction of correctly predicted examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "nbpresent": {
     "id": "1919eb5a-988f-4bee-a646-d110372bbca6"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / len(y_test_pred)\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# utility function for plotting confusion matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def plot_confmat(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Plot the confusion matrix and save to user_files dir\n",
    "    \"\"\"\n",
    "    conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "    fig = plt.figure(figsize=(9,9))\n",
    "    ax = fig.add_subplot(111)\n",
    "    sns.heatmap(conf_matrix,\n",
    "                annot=True,\n",
    "                fmt='.0f')\n",
    "    plt.title('Confusion matrix')\n",
    "    ax.set_xticklabels( classes)\n",
    "    ax.set_yticklabels( classes)\n",
    "    plt.ylabel('True')\n",
    "    plt.xlabel('Predicted')\n",
    "    \n",
    "plot_confmat(y_test, y_test_pred)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "09892b80-b73f-41f3-8671-04ebc8f58ece"
    }
   },
   "source": [
    "# k-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "nbpresent": {
     "id": "4d4d5599-4959-4aa9-8250-7ccd99c0eef6"
    }
   },
   "outputs": [],
   "source": [
    "num_folds = 5\n",
    "\n",
    "k_choices = [1, 3, 5, 7, 9, 10, 12, 15, 18, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "eeb5aeda-0ce5-4581-9fbe-e7605376384a"
    }
   },
   "source": [
    "We Split up the training data into folds. After splitting, `X_train_folds` and    \n",
    "`y_train_folds` should each be lists of length `num_folds`, where                \n",
    "`y_train_folds[i]` is the label vector for the points in `X_train_folds[i]`  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "nbpresent": {
     "id": "50f9138b-3378-411f-96a1-5e3fe013e396"
    }
   },
   "outputs": [],
   "source": [
    "num_train = X_train.shape[0]\n",
    "fold_size = np.ceil(num_train/num_folds).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "267d72f8-6485-4abd-a1d7-d26b4be5a6cc"
    }
   },
   "source": [
    " In the case of `num_train = 5000` and 5 folds, we obtain \n",
    "`X_train_folds = np.split(X_train, [1000, 2000, 3000, 4000])`\n",
    "`y_train_folds = np.split(y_train, [1000, 2000, 3000, 4000])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "nbpresent": {
     "id": "ca3a1d8c-4b8a-42d6-94e7-793e87cebdea"
    }
   },
   "outputs": [],
   "source": [
    "X_train_folds = np.split(X_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n",
    "y_train_folds = np.split(y_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e1be1d21-0776-4587-9b88-d38e804eab71"
    }
   },
   "source": [
    "A dictionary holding the accuracies for different values of $k$ that we find\n",
    "when running cross-validation. After running cross-validation,\n",
    "`k_to_accuracies[k]` should be a list of length num_folds giving the different\n",
    "accuracy values that we found when using that value of $k$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "nbpresent": {
     "id": "05e1ac10-1a25-4740-a21b-8b067116fd69"
    }
   },
   "outputs": [],
   "source": [
    "k_to_accuracies = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f97b560b-929b-4a1f-90ee-3cf17ecef7e6"
    }
   },
   "source": [
    "We perform $k$-fold cross validation to find the best value of $k$. For each     \n",
    "possible value of $k$, run the $k$-nearest-neighbor algorithm `num_folds` times,   \n",
    "where in each case you use all but one of the folds as training data and the \n",
    "last fold as a validation set. Store the accuracies for all fold and all     \n",
    "values of $k$ in the `k_to_accuracies` dictionary.       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "bc2a21b5-4387-4bfc-8851-abcf62acaafc"
    }
   },
   "outputs": [],
   "source": [
    "for k in k_choices:\n",
    "  \n",
    "  k_to_accuracies[k] = []\n",
    "  classifier = KNearestNeighbor_L1()\n",
    "  for i in range(num_folds):\n",
    "      X_cv_training = np.concatenate([x for k, x in enumerate(X_train_folds) if k!=i], axis=0)\n",
    "      y_cv_training = np.concatenate([x for k, x in enumerate(y_train_folds) if k!=i], axis=0)\n",
    "      classifier.train(X_cv_training, y_cv_training)\n",
    "      dists = classifier.compute_distances_two_loops(X_train_folds[i])\n",
    "      y_test_pred = classifier.predict_labels(dists, k=k)\n",
    "      k_to_accuracies[k].append(np.mean(y_train_folds[i] == y_test_pred))\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c24db8cd-04a8-45a6-b15e-24194bb42248"
    }
   },
   "source": [
    "We print out the computed accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "972c66f2-03ea-4de0-8ac6-a564c3365f50"
    }
   },
   "outputs": [],
   "source": [
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "57f5291f-1e32-456f-b84f-76eba7b40d44"
    }
   },
   "source": [
    "We plot the raw observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "a028040f-a7a6-4b61-904d-48090dcbbe8d"
    }
   },
   "outputs": [],
   "source": [
    "for k in k_choices:\n",
    "  accuracies = k_to_accuracies[k]\n",
    "  plt.scatter([k] * len(accuracies), accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6a867f1e-9207-4d0d-adf9-7884532ed06e"
    }
   },
   "source": [
    "We plot the trend line with error bars that correspond to standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "caf9f446-5155-42db-a69b-46f1d7f06322"
    }
   },
   "outputs": [],
   "source": [
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  }
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