{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# kNN 分类算法\n", "\n", "\n", "K最近邻(k-Nearest Neighbor,kNN)分类算法,是一个理论上比较成熟的方法,也是最简单的机器学习算法之一。该方法的思路是:***如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别,则该样本也属于这个类别***。\n", "\n", "kNN方法虽然从原理上也依赖于[极限定理](https://baike.baidu.com/item/%E6%9E%81%E9%99%90%E5%AE%9A%E7%90%86/13672616),但在类别决策时,只与极少量的相邻样本有关。由于kNN方法主要靠周围有限的邻近的样本,而不是靠判别类域的方法来确定所属类别的,因此对于类域的交叉或重叠较多的待分样本集来说,kNN方法较其他方法更为适合。\n", "\n", "kNN算法不仅可以用于分类,还可以用于回归。通过找出一个样本的`k`个最近邻居,将这些邻居的属性的平均值赋给该样本,就可以得到该样本的属性。更有用的方法是将不同距离的邻居对该样本产生的影响给予不同的权值(weight),如权值与距离成正比(组合函数)。\n", "\n", "该算法存在的问题:\n", "1. 当样本不平衡时,如一个类的样本数量很大,而其他类样本数量很小时,有可能导致当输入一个新样本时,该样本的K个邻居中大数量类的样本占多数。在这种情况下可能会产生误判的结果。因此我们需要减少数量对运行结果的影响。可以采用权值的方法(和该样本距离小的邻居权值大)来改进。\n", "2. 计算量较大,因为对每一个待分类的数据都要计算它到全体已知样本的距离,才能求得它的K个最近邻点。目前常用的解决方法是事先对已知样本点进行剪辑,事先去除对分类作用不大的样本。该算法比较适用于样本容量比较大的类域的自动分类,而那些样本容量较小的类域采用这种算法比较容易产生误分。\n", "\n", "kNN可以说是一种最直接的用来分类未知数据的方法。基本通过下面这张图跟文字说明就可以明白kNN是干什么的\n", "![knn](images/knn.png)\n", "\n", "简单来说,kNN可以看成:**有那么一堆你已经知道分类的数据,然后当一个新数据进入的时候,就开始跟训练数据里的每个点求距离,然后挑选这个训练数据最近的K个点,看看这几个点属于什么类型,然后用少数服从多数的原则,给新数据归类**。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 算法步骤:\n", "\n", "输入:\n", "* 训练数据: $T=\\{(x_1,y_1),(x_2,y_2), ..., (x_N,y_N)\\}$, 其中$x_i \\in X=R^n$,$y_i \\in Y = {0, 1, ..., K-1}$,i=1,2...N\n", "* 用户输入数据:$x_u$\n", "\n", "输出:预测的最优类别$y_{pred}$\n", "\n", "\n", "1. 准备数据,对数据进行预处理;\n", "2. 计算测试数据与各个训练数据之间的**距离**;\n", "3. 按照距离的递增关系进行排序;\n", "4. 选取距离最小的`k`个点;\n", "5. 确定前`k`个点所在类别的出现频率;\n", "6. 返回前`k`个点中出现频率最高的类别作为测试数据的预测分类。\n", "\n", "\n", "\n", "**深入思考:**\n", "* 上述的处理过程,难点有哪些?\n", "* 每个处理步骤如何用程序语言来描述?\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 距离计算\n", "\n", "要度量空间中点距离的话,有好几种度量方式,比如常见的曼哈顿距离计算、欧式距离计算等等。不过通常 KNN 算法中使用的是欧式距离。这里只是简单说一下,拿二维平面为例,二维空间两个点的欧式距离计算公式如下:\n", "$$\n", "d = \\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}\n", "$$\n", "\n", "在二维空间其实就是计算 $(x_1,y_1)$ 和 $(x_2, y_2)$ 的距离。拓展到多维空间,则公式变成:\n", "$$\n", "d(p, q) = \\sqrt{ (p_1-q_1)^2 + (p_1-q_1)^2 + ... + (p_n-q_n)^2 } = \\sqrt{ \\sum_{i=1,n} (p_i-q_i)^2}\n", "$$\n", "\n", "这样我们就明白了如何计算距离。kNN 算法最简单粗暴的就是将 `预测点` 与 `所有点` 距离进行计算,然后保存并排序,选出前面 k 个值看看哪些类别比较多。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 2. 机器学习的思维模型\n", "\n", "![machine learning - methodology](images/ml_methodology.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. 生成数据" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# data generation\n", "np.random.seed(314)\n", "\n", "data_size1 = 10000\n", "x1 = np.random.randn(data_size1, 2) + np.array([4,4])\n", "y1 = [0 for _ in range(data_size1)]\n", "\n", "data_size2 = 10000\n", "x2 = np.random.randn(data_size2, 2)*2 + np.array([10,10])\n", "y2 = [1 for _ in range(data_size2)]\n", "\n", "\n", "# all sample data\n", "x = np.concatenate((x1, x2), axis=0)\n", "y = np.concatenate((y1, y2), axis=0)\n", "\n", "data_size_all = data_size1 + data_size2\n", "shuffled_index = np.random.permutation(data_size_all)\n", "x = x[shuffled_index]\n", "y = y[shuffled_index]\n", "\n", "# split train & test\n", "split_index = int(data_size_all*0.7)\n", "x_train = x[:split_index]\n", "y_train = y[:split_index]\n", "x_test = x[split_index:]\n", "y_test = y[split_index:]\n", "\n", "\n", "# plot data\n", "plt.scatter(x_train[:,0], x_train[:,1], c=y_train, marker='.')\n", "plt.title(\"train data\")\n", "plt.show()\n", "plt.scatter(x_test[:,0], x_test[:,1], c=y_test, marker='.')\n", "plt.title(\"test data\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. 最简单的程序实现" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import operator\n", "\n", "def knn_distance(v1, v2):\n", " return np.sum(np.square(v1-v2))\n", "\n", "def knn_vote(ys):\n", " method = 1\n", " \n", " # method 1\n", " if method == 1:\n", " vote_dict = {}\n", " for y in ys:\n", " if y not in vote_dict.keys():\n", " vote_dict[y] = 1\n", " else:\n", " vote_dict[y] += 1\n", " sorted_vote_dict = sorted(vote_dict.items(), \\\n", " #key=operator.itemgetter(1), \\\n", " key=lambda x:x[1], \\\n", " reverse=True)\n", " \n", " return sorted_vote_dict[0][0]\n", " \n", " # method 2\n", " if method == 2:\n", " maxv = 0\n", " maxk = 0\n", " \n", " vote_dict = {}\n", " for y in ys:\n", " if y not in vote_dict.keys():\n", " vote_dict[y] = 1\n", " else:\n", " vote_dict[y] += 1\n", " \n", " for y in np.unique(ys):\n", " if maxv < vote_dict[y]:\n", " maxv = vote_dict[y]\n", " maxk = y\n", " return maxk\n", " \n", "def knn_predict(x, train_x, train_y, k=3):\n", " dist_arr = [knn_distance(x, train_x[j]) for j in range(len(train_x))]\n", " sorted_index = np.argsort(dist_arr)\n", " top_k_index = sorted_index[:k]\n", " ys=train_y[top_k_index]\n", " return knn_vote(ys)\n", " \n", "\n", "#a = knn_predict(x_train[0], x_train, y_train)\n", "\n", "y_train_est = [knn_predict(x_train[i], x_train, y_train) for i in range(len(x_train))]\n", "print(y_train_est)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train Accuracy: 100.000000%\n" ] } ], "source": [ "n_correct = 0\n", "for i in range(len(x_train)):\n", " if y_train_est[i] == y_train[i]:\n", " n_correct += 1\n", "accuracy = n_correct / len(x_train) * 100.0\n", "print(\"Train Accuracy: %f%%\" % accuracy)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Accuracy: 96.666667%\n", "58 60\n" ] } ], "source": [ "y_test_est = [knn_predict(x_test[i], x_train, y_train, 3) for i in range(len(x_test))]\n", "n_correct = 0\n", "for i in range(len(x_test)):\n", " if y_test_est[i] == y_test[i]:\n", " n_correct += 1\n", "accuracy = n_correct / len(x_test) * 100.0\n", "print(\"Test Accuracy: %f%%\" % accuracy)\n", "print(n_correct, len(x_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. 通过类实现kNN程序" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import operator\n", "\n", "class KNN(object):\n", "\n", " def __init__(self, k=3):\n", " self.k = k\n", "\n", " def fit(self, x, y):\n", " self.x = x\n", " self.y = y\n", " return self\n", "\n", " def _square_distance(self, v1, v2):\n", " return np.sum(np.square(v1-v2))\n", "\n", " def _vote(self, ys):\n", " ys_unique = np.unique(ys)\n", " vote_dict = {}\n", " for y in ys:\n", " if y not in vote_dict.keys():\n", " vote_dict[y] = 1\n", " else:\n", " vote_dict[y] += 1\n", " sorted_vote_dict = sorted(vote_dict.items(), key=operator.itemgetter(1), reverse=True)\n", " return sorted_vote_dict[0][0]\n", "\n", " def predict(self, x):\n", " y_pred = []\n", " for i in range(len(x)):\n", " dist_arr = [self._square_distance(x[i], self.x[j]) for j in range(len(self.x))]\n", " sorted_index = np.argsort(dist_arr)\n", " top_k_index = sorted_index[:self.k]\n", " y_pred.append(self._vote(ys=self.y[top_k_index]))\n", " return np.array(y_pred)\n", "\n", " def score(self, y_true=None, y_pred=None):\n", " if y_true is None and y_pred is None:\n", " y_pred = self.predict(self.x)\n", " y_true = self.y\n", " score = 0.0\n", " for i in range(len(y_true)):\n", " if y_true[i] == y_pred[i]:\n", " score += 1\n", " score /= len(y_true)\n", " return score" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train accuracy: 100.000000 %\n", "test accuracy: 96.666667 %\n" ] } ], "source": [ "# data preprocessing\n", "#x_train = (x_train - np.min(x_train, axis=0)) / (np.max(x_train, axis=0) - np.min(x_train, axis=0))\n", "#x_test = (x_test - np.min(x_test, axis=0)) / (np.max(x_test, axis=0) - np.min(x_test, axis=0))\n", "\n", "# knn classifier\n", "clf = KNN(k=3)\n", "train_acc = clf.fit(x_train, y_train).score() * 100.0\n", "\n", "y_test_pred = clf.predict(x_test)\n", "test_acc = clf.score(y_test, y_test_pred) * 100.0\n", "\n", "print('train accuracy: %f %%' % train_acc)\n", "print('test accuracy: %f %%' % test_acc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. sklearn program" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feature dimensions: (1797, 64)\n", "Label dimensions: (1797,)\n" ] } ], "source": [ "#% matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets, neighbors, linear_model\n", "\n", "# load data\n", "digits = datasets.load_digits()\n", "X_digits = digits.data\n", "y_digits = digits.target\n", "\n", "print(\"Feature dimensions: \", X_digits.shape)\n", "print(\"Label dimensions: \", y_digits.shape)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot sample images\n", "nplot = 10\n", "fig, axes = plt.subplots(nrows=1, ncols=nplot)\n", "\n", "for i in range(nplot):\n", " img = X_digits[i].reshape(8, 8)\n", " axes[i].imshow(img)\n", " axes[i].set_title(y_digits[i])\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# split train / test data\n", "n_samples = len(X_digits)\n", "n_train = int(0.4 * n_samples)\n", "\n", "X_train = X_digits[:n_train]\n", "y_train = y_digits[:n_train]\n", "X_test = X_digits[n_train:]\n", "y_test = y_digits[n_train:]\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNN score: 0.953661\n", "LogisticRegression score: 0.927711\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bushuhui/anaconda3/envs/dl/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" ] } ], "source": [ "# do KNN classification\n", "knn = neighbors.KNeighborsClassifier()\n", "logistic = linear_model.LogisticRegression()\n", "\n", "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\n", "print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. 深入思考\n", "\n", "* 如果输入的数据非常多,怎么快速进行距离计算?\n", " - [kd-tree](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree) \n", " - Fast Library for Approximate Nearest Neighbors (FLANN)\n", " - [PyNNDescent for fast Approximate Nearest Neighbors](https://pynndescent.readthedocs.io/en/latest/)\n", "* 如何选择最好的`k`?\n", " - https://zhuanlan.zhihu.com/p/143092725\n", "* kNN存在的问题?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 参考资料\n", "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n", "* [knn算法的原理与实现](https://zhuanlan.zhihu.com/p/36549000)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }