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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "df630e55-57dd-470a-849b-f1bc90ac719e"
}
},
"source": [
"# 1. Importing and Visualization of CIFAR-10 Dataset"
]
},
{
"cell_type": "code",
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"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",
"metadata": {
"nbpresent": {
"id": "f77bd9ec-de3b-4c56-b08d-4a65f0780408"
}
},
"outputs": [
{
"data": {
"text/plain": [
"dict"
]
},
"execution_count": 3,
"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",
"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": 4,
"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",
"metadata": {
"nbpresent": {
"id": "fe299a35-c930-4078-97b7-c9b67f42ec42"
}
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 5,
"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",
"metadata": {
"nbpresent": {
"id": "46a97575-36c0-4920-a8dc-762e94239b7e"
}
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 6,
"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",
"metadata": {
"nbpresent": {
"id": "b012720d-81f8-455d-8ce7-bfca64a842c8"
}
},
"data": {
"text/plain": [
"(10000, 3072)"
]
},
"execution_count": 7,
"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",
"metadata": {
"nbpresent": {
"id": "49c776cb-c8aa-461b-a0da-4f4d38342e2e"
}
},
"outputs": [
{
"data": {
"text/plain": [
"10000"
]
},
"execution_count": 8,
"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",
"metadata": {
"nbpresent": {
"id": "438920f4-774e-4e94-9b7c-30a2106d163c"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[6, 9, 9, 4, 1, 1, 2, 7, 8, 3]"
]
},
"execution_count": 9,
"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",
"metadata": {
"nbpresent": {
"id": "7617a699-c3d5-434f-97a5-3443489ac9db"
}
},
"outputs": [
{
"data": {
"text/plain": [
"dtype('uint8')"
]
},
"execution_count": 10,
"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",
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"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",
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"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",
"metadata": {
"nbpresent": {
"id": "5c85918c-f89e-4156-8cdd-ca38d14afbb9"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 3072)"
]
},
"execution_count": 13,
"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",
"metadata": {
"nbpresent": {
"id": "5f913d95-aa49-4727-8c6f-5630cbf59741"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(10000,)"
]
},
"execution_count": 14,
"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",
"metadata": {
"nbpresent": {
"id": "a0eb7a33-19c9-46e4-b471-6f7904389177"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(50000, 3072)"
]
},
"execution_count": 15,
"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",
"metadata": {
"nbpresent": {
"id": "d699e7a7-efc0-421f-bd8d-2d2b34a09516"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(50000,)"
]
},
"execution_count": 16,
"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. "
]
},
{
"cell_type": "code",
"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"
}
],
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"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": {},
"source": [
"\n",
"- By means of `reshape` we can convert an array from one shape to another without \n",
"copying any data. To do this, we pass a tuple indicating the new shape to the `reshape` array instance method. \n",
"\n",
"- By default, `NumPy` arrays are created in _row major_ order. Spatially this means, that if we have a two-dimensional array of data, the items in each row of the array are stored in adjacent memory locations. In the case of a three-dimensional array of data, the items along `axis=2` are stored in adjacent order. Since the first 32 entries of the array `X_train[0]` are the red channel values of the first row of the image, etc., we need to pass the tuple $(3,32,32)$ to `reshape`. In conclusion, when __reshaping__ the array, higher order dimensions are traversed _first_ (e.g. axis $ 2 $ before advancing on axis $ 1 $.) \n",
"\n",
"- `plt.imshow` needs for each inner list the values representing a pixel. Here, with \n",
"an RGB image, there are 3 values. We thus need to transpose the array : the RGB values need to be located along `axis=2`. \n",
"\n",
"- Using ndarray's `astype` method, we can cast an array from one `dtype` to another. `uint8` represents unsigned 8-bit integer types. Why 8 bits? Most displays can only render 8 bits per channel worth of color gradation. Why can they only render 8 bits/channel? Because that is about all the human eye can see."
]
},
{
"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",
"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"
}
],
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"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": {},
"source": [
"- When iterating over a sequence we often want to keep track of the index of the \n",
"current item. Python's built-in function `enumerate` returns a sequence of $i$, value \n",
"tuples.\n",
"\n",
"- `np.flatnonzero` returns indices that are non-zero in the (flattened version of) `y_train == y`, that is, it returns the indices for the elements that are `True`.\n",
"\n",
"- `np.random.choice(..., replace=False)` generates a random sample from a given 1-D array without replacement.\n",
"\n",
"\n",
"- The `subplot()` command specifies `numrows`, `numcols`, `fignum` where `fignum` ranges from $ 1 $ to `numrows*numcols`."
]
},
{
"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",
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"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",
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"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": {},
"source": [
"- Methods within a class are defined in much the same way as functions, that is, using `def`. Every defined class has a _special method_ called `__init__()` `Python` runs automatically whenever we create a new _instance_ based on the `NearestNeighbor` \n",
"class. The `self` parameter is required in the method definition, and it must come first \n",
"before the other parameters. It must be included in the definition because when `Python` \n",
"calls this `__init__` method later (to create an instance of `NearestNeighbor`), \n",
"the method call will automatically pass the `self` argument. Every method call associated with a class automatically passes `self`, which is a reference to the instance itself; \n",
"it gives the individual instance access to the attributes and methods in the class. In summary, when you invoke a class method on an object instance, `Python` \n",
"arranges for the first argument to be the invoking object instance, which is always \n",
"assigned to each method's `self` argument.\n",
"\n",
"- The two variables `self.X_train` and `self.y_train` each have the prefix `self`. Any variable prefixed with `self` is available to every method in the class, and we will also be able to access these variables through any instance created from the class. `self.X_train = X` takes the value stored in the parameter `X` and stores it in the variable `self.X_train`, which is then attached to the instance being created. The same process happens with `self.y_train = y`. \n",
"\n",
"- Variables that are accessible through instances like this are called _attributes_.\n",
"`np.zeros` produces an array of $ 0 $'s, here the size is `num_test`.\n",
"\n",
"- `np.sum` returns the sum of all elements in the array or along an axis. Zero-length \n",
"arrays have sum $ $ 0.\n",
"\n",
"- `np.argmin` returns the index of minimum element.\n",
"\n",
"- When Python reads the line `nn = NearestNeighbor()`, it calls the `__init__()` method in `NearestNeighbor()`. The `__init__()` method creates an instance representing this particular nearest neighbor classifier. The `__init__()` method has no explicit return statement, but Python automatically returns an instance representing this nearest neighbor classifier. We store that instance in the variable `nn`. The naming convention is helpful here: we can usually assume that a capitalized name like `NearestNeighbor()` refers to a class, and a lowercase name like `nn` refers to a single instance created from a class.\n",
"\n",
"\n",
"- `np.argsort` returns the indices that would sort an array.\n",
"\n",
"- `np.bincount(x)` counts the number of occurrences of each value in an array of non-negative integers. The number of bins (of size 1) is one larger than the largest value in $ x $.\n",
"\n",
"- Instead of a loop that contains a _broadcasting_ process with a 2D array \n",
"\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",
"we can speed up this process by broadcasting with a 3D array \n",
"\n",
"`dists = np.sqrt(np.square((self.X_train[:, np.newaxis, :] - X)).sum(axis=2))`\n",
"\n",
"- The _Broadcasting rule_ states: two arrays are compatible for broadcasting if for each \n",
"_trailing dimension_ (that is, starting from the end of an array), the axis lengths match or if either of the lengths is 1. In the case of `self._train[:, np.newaxis, :]`, the trailing dimension is $2$ and the axis length $D$. The trailing dimension of `X` is 1 and has axis length $D$. Thus, they match. Broadcasting is then performed over the missing and / or length $ 1 $ dimensions. If we need to add a new axis with length $ 1 $ specifically for broadcasting purposes, `NumPy` arrays offer a special syntax for inserting new axes by indexing. We use the special `np.newaxis` attribute along with full slices to insert the new axis. We may imagine that `num_test` copies of `self.X_train` are tiled up along `axis=1` of `self.X_train[:, np.newaxis, :]`. On the other hand, `num_train` copies of `X` are tiled up on top of each other. First, the subtraction is performed along `axis=2`. Then, it is performed along `axis=1`, and finally over `axis=0`. The resulting shape of the subtraction then is (`num_train`, `num_test`, D). Since this runs into memory issues, we need to rewrite it as follows:\n",
"\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",
"which yields an array with shape (`num_test`, `num_train`, D)\n",
"\n",
"- It is important to cast the data files as `float`."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "91c8998c-f531-4774-98ca-6c9631050fd3"
}
},
"source": [
"Create an instance nn from the class KNearestNeighbor"
]
},
{
"cell_type": "code",
"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",
"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",
"metadata": {
"nbpresent": {
"id": "d87bb3a8-6338-4957-ac73-4c81b87821eb"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(500, 5000)"
]
},
"execution_count": 23,
"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",
"metadata": {
"nbpresent": {
"id": "ae3a05a2-a3e6-4e65-a59f-0204411f57f9"
}
},
"outputs": [
{
"data": {
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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",
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"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",
"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",
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"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",
"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",
"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"
}
],
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"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",
"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"
]
}
],
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"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",
"metadata": {
"nbpresent": {
"id": "1d3c6b0c-9a33-4f71-b283-0b1eb8061e77"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Two loop version took 62.240666 seconds\n",
"One loop version took 75.840250 seconds\n",
"No loop version took 0.912069 seconds\n"
]
}
],
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"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",
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"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",
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"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",
"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",
"outputs": [
{
"data": {
"text/plain": [
"(1000, 3072)"
]
},
"execution_count": 35,
"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",
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"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",
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"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",
"metadata": {
"nbpresent": {
"id": "d7c42393-850e-4329-91db-5c052fe247e0"
}
},
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"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",
"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",
"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"
}
],
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"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",
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"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 L1 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",
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"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",
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"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",
"metadata": {
"nbpresent": {
"id": "f6ecd69e-e8b4-44a5-8ec1-8aeb47fbc5b5"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(500, 5000)"
]
},
"execution_count": 44,
"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",
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"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",
"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",
"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"
}
],
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
"metadata": {
"nbpresent": {
"id": "972c66f2-03ea-4de0-8ac6-a564c3365f50"
}
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k = 1, accuracy = 0.291000\n",
"k = 1, accuracy = 0.313000\n",
"k = 1, accuracy = 0.294000\n",
"k = 1, accuracy = 0.275000\n",
"k = 1, accuracy = 0.308000\n",
"k = 3, accuracy = 0.269000\n",
"k = 3, accuracy = 0.299000\n",
"k = 3, accuracy = 0.290000\n",
"k = 3, accuracy = 0.278000\n",
"k = 3, accuracy = 0.296000\n",
"k = 5, accuracy = 0.275000\n",
"k = 5, accuracy = 0.311000\n",
"k = 5, accuracy = 0.301000\n",
"k = 5, accuracy = 0.314000\n",
"k = 5, accuracy = 0.309000\n",
"k = 7, accuracy = 0.280000\n",
"k = 7, accuracy = 0.329000\n",
"k = 7, accuracy = 0.313000\n",
"k = 7, accuracy = 0.320000\n",
"k = 7, accuracy = 0.313000\n",
"k = 9, accuracy = 0.291000\n",
"k = 9, accuracy = 0.314000\n",
"k = 9, accuracy = 0.310000\n",
"k = 9, accuracy = 0.322000\n",
"k = 9, accuracy = 0.315000\n",
"k = 10, accuracy = 0.289000\n",
"k = 10, accuracy = 0.312000\n",
"k = 10, accuracy = 0.320000\n",
"k = 10, accuracy = 0.323000\n",
"k = 10, accuracy = 0.313000\n",
"k = 12, accuracy = 0.295000\n",
"k = 12, accuracy = 0.320000\n",
"k = 12, accuracy = 0.324000\n",
"k = 12, accuracy = 0.332000\n",
"k = 12, accuracy = 0.318000\n",
"k = 15, accuracy = 0.287000\n",
"k = 15, accuracy = 0.324000\n",
"k = 15, accuracy = 0.317000\n",
"k = 15, accuracy = 0.319000\n",
"k = 15, accuracy = 0.321000\n",
"k = 18, accuracy = 0.289000\n",
"k = 18, accuracy = 0.321000\n",
"k = 18, accuracy = 0.307000\n",
"k = 18, accuracy = 0.319000\n",
"k = 18, accuracy = 0.306000\n",
"k = 20, accuracy = 0.287000\n",
"k = 20, accuracy = 0.327000\n",
"k = 20, accuracy = 0.309000\n",
"k = 20, accuracy = 0.307000\n",
"k = 20, accuracy = 0.306000\n",
"k = 50, accuracy = 0.285000\n",
"k = 50, accuracy = 0.301000\n",
"k = 50, accuracy = 0.294000\n",
"k = 50, accuracy = 0.290000\n",
"k = 50, accuracy = 0.293000\n",
"k = 100, accuracy = 0.283000\n",
"k = 100, accuracy = 0.285000\n",
"k = 100, accuracy = 0.279000\n",
"k = 100, accuracy = 0.285000\n",
"k = 100, accuracy = 0.277000\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",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "57f5291f-1e32-456f-b84f-76eba7b40d44"
}
},
"source": [
"We plot the raw observations."
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "a028040f-a7a6-4b61-904d-48090dcbbe8d"
}
},
"outputs": [
{
"data": {
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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": "6a867f1e-9207-4d0d-adf9-7884532ed06e"
}
},
"source": [
"We plot the trend line with error bars that correspond to standard deviation."
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