diff --git a/.DS_Store b/.DS_Store index 8e14b28..4cffb45 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/_toc.yml b/_toc.yml index 793ee5d..172b675 100644 --- a/_toc.yml +++ b/_toc.yml @@ -21,6 +21,7 @@ parts: chapters: - file: docs/分类/决策树 - file: docs/分类/集成方法 + - file: docs/分类/支持向量机 - caption: 降维 chapters: - file: docs/降维/线性代数 diff --git "a/docs/\345\210\206\347\261\273/\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272.ipynb" "b/docs/\345\210\206\347\261\273/\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272.ipynb" new file mode 100644 index 0000000..70bf114 --- /dev/null +++ "b/docs/\345\210\206\347\261\273/\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272.ipynb" @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ae968ff7", + "metadata": {}, + "source": [ + "# 支持向量机\n" + ] + }, + { + "cell_type": "markdown", + "id": "f67afe2e", + "metadata": {}, + "source": [ + "## 核函数\n", + "\n", + "在支持向量机(SVM)中,核函数(kernel function)是一种用于将数据映射到高维特征空间的函数。核函数的作用是在原始特征空间中隐式地计算样本之间的相似性或内积,从而使非线性问题可以通过线性分类器进行处理。\n", + "\n", + "核函数的关键思想是将低维空间中线性不可分的模式转换为高维空间中线性可分的模式。这种转换可以使SVM能够建立一个决策边界,将不同类别的样本正确地分开。通过引入核函数,我们无需直接计算高维特征空间,而只需计算低维空间中的核函数值。\n", + "\n", + "常见的核函数包括:\n", + "\n", + "线性核函数(linear kernel):在原始特征空间中直接计算样本之间的内积,即K(x, y) = x·y。\n", + "\n", + "多项式核函数(polynomial kernel):将样本映射到多项式特征空间,通过计算多项式函数来度量样本之间的相似性。\n", + "\n", + "高斯核函数(Gaussian kernel),也称为径向基函数(Radial Basis Function,RBF)核函数:将样本映射到无限维的特征空间,通过计算样本之间的欧氏距离来度量相似性。\n", + "\n", + "sigmoid核函数:将样本映射到双曲正切函数的特征空间,通过计算双曲正切函数来度量样本之间的相似性。\n", + "\n", + "下面对比下几种方法:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5aded698", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import make_circles\n", + "from sklearn.svm import SVC\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 创建一个非线性的二维数据集\n", + "X, y = make_circles(n_samples=100, noise=0.1, factor=0.4, random_state=42)\n", + "\n", + "# 定义不同核函数的名称和参数\n", + "kernels = ['linear', 'poly', 'rbf']\n", + "parameters = [{'C': 1}, {'degree': 3, 'C': 1}, {'gamma': 'scale', 'C': 1}]\n", + "\n", + "# 绘制不同核函数的分类结果\n", + "plt.figure(figsize=(12, 4))\n", + "for i, kernel in enumerate(kernels):\n", + " # 创建SVC模型并使用指定核函数进行训练\n", + " model = SVC(kernel=kernel, **parameters[i])\n", + " model.fit(X, y)\n", + " \n", + " # 在二维空间中绘制分类结果\n", + " plt.subplot(1, 3, i + 1)\n", + " plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)\n", + " plt.title(f\"Kernel: {kernel}\")\n", + " \n", + " # 绘制决策边界\n", + " ax = plt.gca()\n", + " xlim = ax.get_xlim()\n", + " ylim = ax.get_ylim()\n", + " xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 100),\n", + " np.linspace(ylim[0], ylim[1], 100))\n", + " Z = model.decision_function(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + " plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5)\n", + " \n", + " plt.xlabel('X')\n", + " plt.ylabel('Y')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "12be94bb", + "metadata": {}, + "source": [ + "## 多分类 OvO vs OvR\n", + "在多分类问题中,SVM支持两种常见的策略:One-vs-One(OvO)和One-vs-Rest(OvR)。它们之间的区别在于处理多个类别的方式。\n", + "\n", + "One-vs-One和One-vs-Rest两种多分类策略的主要区别:\n", + "\n", + "| | One-vs-One (OvO) | One-vs-Rest (OvR) |\n", + "|----------|-------------------|--------------------|\n", + "| 模型数量 | N * (N-1) / 2 | N |\n", + "| 训练时间 | 较长 | 较短 |\n", + "| 内存消耗 | 较大 | 较小 |\n", + "| 样本数量 | 每个模型较少 | 每个模型较多 |\n", + "| 预测效果 | 可能更准确 | 简洁但可能稍差 |\n", + "\n", + "在这个表格中,\"N\"表示类别的总数。\n", + "* One-vs-One策略需要构建更多的模型,训练时间更长,消耗更多的内存,但在某些情况下可能提供更准确的预测结果。\n", + "* One-vs-Rest策略只需要构建N个模型,训练时间和内存消耗较少,但预测结果可能相对简洁且略有不准确。\n", + "\n", + "一般来说,当类别数量较少时,OvR策略可能更简洁有效。而当类别数量较多时,OvO策略可能更准确,但也需要更多的计算资源和时间。\n", + "\n", + "从性能考虑,默认是OvR。\n", + "\n", + "下面对比下这两种策略:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dd11abb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "One-vs-One 准确率: 1.0\n", + "One-vs-Rest 准确率: 1.0\n" + ] + } + ], + "source": [ + "# 导入必要的库\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# 加载数据集\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "# 将数据集拆分为训练集和测试集\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# 创建一个SVC模型(使用one-vs-one策略)\n", + "model_ovo = SVC(decision_function_shape='ovo')\n", + "# 使用训练数据来训练模型\n", + "model_ovo.fit(X_train, y_train)\n", + "# 使用训练后的模型对测试数据进行预测\n", + "y_pred_ovo = model_ovo.predict(X_test)\n", + "# 计算预测准确率\n", + "accuracy_ovo = accuracy_score(y_test, y_pred_ovo)\n", + "print(\"One-vs-One 准确率:\", accuracy_ovo)\n", + "\n", + "# 创建一个SVC模型(使用one-vs-rest策略)\n", + "model_ovr = SVC(decision_function_shape='ovr')\n", + "# 使用训练数据来训练模型\n", + "model_ovr.fit(X_train, y_train)\n", + "# 使用训练后的模型对测试数据进行预测\n", + "y_pred_ovr = model_ovr.predict(X_test)\n", + "# 计算预测准确率\n", + "accuracy_ovr = accuracy_score(y_test, y_pred_ovr)\n", + "print(\"One-vs-Rest 准确率:\", accuracy_ovr)" + ] + }, + { + "cell_type": "markdown", + "id": "3d235dd6", + "metadata": {}, + "source": [ + "\n", + "## SVC、NuSVC和LinearSVC\n", + "\n", + "| | SVC | NuSVC | LinearSVC |\n", + "|--------|--------------------|---------------------------|------------------------|\n", + "| 算法 | C-SVM | ν-SVM | 线性SVM |\n", + "| 核函数 | 可选择不同的核函数 | 可选择不同的核函数 | 不支持核函数 |\n", + "| 分类 | 二分类和多分类 | 二分类和多分类 | 仅线性可分问题 |\n", + "| 训练时间| 长 | 较长 | 较快 |\n", + "| 大型数据集 | 适用 | 适用 | 适用 |\n", + "\n", + "这个表格总结了SVC、NuSVC和LinearSVC三种算法的主要特点\n", + "\n", + "* 如果问题是线性可分的并且需要较快的训练时间,可以选择LinearSVC。\n", + "* 如果问题是非线性的并且对支持向量的数量有一定灵活性要求,可以选择NuSVC。\n", + "* 如果问题是非线性的,并且希望可以选择不同的核函数,可以选择SVC。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "39a7a648", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SVC 准确率: 1.0\n", + "NuSVC 准确率: 1.0\n", + "LinearSVC 准确率: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/xue/tool/anaconda3/envs/data-explore/lib/python3.11/site-packages/sklearn/svm/_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n", + " warnings.warn(\n", + "/Users/xue/tool/anaconda3/envs/data-explore/lib/python3.11/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# 导入必要的库\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import SVC, NuSVC, LinearSVC\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# 加载数据集\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "# 将数据集拆分为训练集和测试集\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# 创建一个SVC模型\n", + "svc_model = SVC()\n", + "# 使用训练数据来训练SVC模型\n", + "svc_model.fit(X_train, y_train)\n", + "# 使用训练后的SVC模型对测试数据进行预测\n", + "svc_pred = svc_model.predict(X_test)\n", + "# 计算SVC模型的预测准确率\n", + "svc_accuracy = accuracy_score(y_test, svc_pred)\n", + "print(\"SVC 准确率:\", svc_accuracy)\n", + "\n", + "# 创建一个NuSVC模型\n", + "nusvc_model = NuSVC()\n", + "# 使用训练数据来训练NuSVC模型\n", + "nusvc_model.fit(X_train, y_train)\n", + "# 使用训练后的NuSVC模型对测试数据进行预测\n", + "nusvc_pred = nusvc_model.predict(X_test)\n", + "# 计算NuSVC模型的预测准确率\n", + "nusvc_accuracy = accuracy_score(y_test, nusvc_pred)\n", + "print(\"NuSVC 准确率:\", nusvc_accuracy)\n", + "\n", + "# 创建一个LinearSVC模型\n", + "linearsvc_model = LinearSVC()\n", + "# 使用训练数据来训练LinearSVC模型\n", + "linearsvc_model.fit(X_train, y_train)\n", + "# 使用训练后的LinearSVC模型对测试数据进行预测\n", + "linearsvc_pred = linearsvc_model.predict(X_test)\n", + "# 计算LinearSVC模型的预测准确率\n", + "linearsvc_accuracy = accuracy_score(y_test, linearsvc_pred)\n", + "print(\"LinearSVC 准确率:\", linearsvc_accuracy)" + ] + }, + { + "cell_type": "markdown", + "id": "fc23cfd6", + "metadata": {}, + "source": [ + "## 非均衡问题\n", + "\n", + "非均衡问题(Imbalanced problem)是指在分类问题中,不同类别的样本数量差异很大,其中一个类别的样本数量远远超过另一个类别。这种情况可能会导致模型对多数类别进行较好的预测,而对少数类别进行较差的预测。\n", + "\n", + "解决非均衡问题的方法有很多,以下是一些常用的方法:\n", + "\n", + "重采样(Resampling):通过欠采样或过采样来平衡数据集中的类别数量。欠采样减少多数类别的样本数量,而过采样增加少数类别的样本数量。这些技术可以通过随机丢弃或复制样本来实现。\n", + "\n", + "类别权重(Class weights):在训练模型时,通过给少数类别分配更高的权重,使得模型在训练过程中更关注少数类别的分类效果。在大多数机器学习算法中,可以使用class_weight参数来设置类别权重。\n", + "\n", + "算法调整(Algorithmic adjustments):一些算法具有内置的处理非均衡数据集的功能,例如决策树算法中的代价敏感训练(Cost-Sensitive Training)。这些算法通过修改损失函数或调整决策阈值来改善少数类别的预测效果。\n", + "\n", + "集成方法(Ensemble methods):使用集成学习方法,如随机森林(Random Forest)或梯度提升树(Gradient Boosting),可以通过结合多个模型的预测结果来改善分类效果。在非均衡问题中,可以使用集成方法来平衡类别之间的预测差异。\n", + "\n", + "生成合成样本(Generate synthetic samples):一些算法可以生成合成的少数类别样本,以增加数据集中少数类别的样本数量。例如,SMOTE(Synthetic Minority Over-sampling Technique)算法可以通过插值生成新的少数类别样本。\n", + "\n", + "下面绘制加权数据集的决策函数图,其中点的大小与其权重成比例。\n", + "\n", + "> 样本加权会重新缩放C参数,这意味着分类器更加强调正确分类这些点。这种影响通常是微妙的。为了突出显示效果,在这里我们特别给异常值赋予更大的权重,使得决策边界的变形非常明显。" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1650e464", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from sklearn import svm\n", + "\n", + "\n", + "def plot_decision_function(classifier, sample_weight, axis, title):\n", + " # plot the decision function\n", + " xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))\n", + "\n", + " Z = classifier.decision_function(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + "\n", + " # plot the line, the points, and the nearest vectors to the plane\n", + " axis.contourf(xx, yy, Z, alpha=0.75, cmap=plt.cm.bone)\n", + " axis.scatter(\n", + " X[:, 0],\n", + " X[:, 1],\n", + " c=y,\n", + " s=100 * sample_weight,\n", + " alpha=0.9,\n", + " cmap=plt.cm.bone,\n", + " edgecolors=\"black\",\n", + " )\n", + "\n", + " axis.axis(\"off\")\n", + " axis.set_title(title)\n", + "\n", + "\n", + "# we create 20 points\n", + "np.random.seed(0)\n", + "X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]\n", + "y = [1] * 10 + [-1] * 10\n", + "sample_weight_last_ten = abs(np.random.randn(len(X)))\n", + "sample_weight_constant = np.ones(len(X))\n", + "# and bigger weights to some outliers\n", + "sample_weight_last_ten[15:] *= 5\n", + "sample_weight_last_ten[9] *= 15\n", + "\n", + "# Fit the models.\n", + "\n", + "# This model does not take into account sample weights.\n", + "clf_no_weights = svm.SVC(gamma=1)\n", + "clf_no_weights.fit(X, y)\n", + "\n", + "# This other model takes into account some dedicated sample weights.\n", + "clf_weights = svm.SVC(gamma=1)\n", + "clf_weights.fit(X, y, sample_weight=sample_weight_last_ten)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + "plot_decision_function(\n", + " clf_no_weights, sample_weight_constant, axes[0], \"Constant weights\"\n", + ")\n", + "plot_decision_function(clf_weights, sample_weight_last_ten, axes[1], \"Modified weights\")\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}