{"id":1212,"date":"2023-07-27T06:54:42","date_gmt":"2023-07-27T06:54:42","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%91%e9%83%a8%e5%88%86%e8%be%b9\/"},"modified":"2023-07-27T06:54:42","modified_gmt":"2023-07-27T06:54:42","slug":"python%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%91%e9%83%a8%e5%88%86%e8%be%b9","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%91%e9%83%a8%e5%88%86%e8%be%b9\/","title":{"rendered":"Python \u4e2d\u7684\u504f\u6700\u5c0f\u4e8c\u4e58\u6cd5\uff08\u4e00\u6b65\u4e00\u6b65\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u673a\u5668\u5b66\u4e60\u4e2d\u6700\u5e38\u89c1\u7684\u95ee\u9898\u4e4b\u4e00\u662f<a href=\"https:\/\/statorials.org\/cn\/\u591a\u91cd\u5171\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u591a\u91cd\u5171\u7ebf\u6027<\/a>\u3002\u5f53\u6570\u636e\u96c6\u4e2d\u7684\u4e24\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u9ad8\u5ea6\u76f8\u5173\u65f6\uff0c\u5c31\u4f1a\u53d1\u751f\u8fd9\u79cd\u60c5\u51b5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u53d1\u751f\u8fd9\u79cd\u60c5\u51b5\u65f6\uff0c\u6a21\u578b\u53ef\u80fd\u80fd\u591f\u5f88\u597d\u5730\u62df\u5408\u8bad\u7ec3\u6570\u636e\u96c6\uff0c\u4f46\u5b83\u53ef\u80fd\u5728\u4ece\u672a\u89c1\u8fc7\u7684\u65b0\u6570\u636e\u96c6\u4e0a\u8868\u73b0\u4e0d\u4f73\uff0c\u56e0\u4e3a\u5b83\u4e0e\u8bad\u7ec3\u6570\u636e\u96c6<a href=\"https:\/\/statorials.org\/cn\/\u673a\u5668\u5b66\u4e60\u8fc7\u5ea6\u62df\u5408\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u8fc7\u5ea6\u62df\u5408<\/a>\u3002\u8bad\u7ec3\u96c6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u4e00\u79cd\u79f0\u4e3a <a href=\"https:\/\/statorials.org\/cn\/\u504f\u6700\u5c0f\u4e8c\u4e58\u6cd5\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u504f\u6700\u5c0f\u4e8c\u4e58\u6cd5<\/a>\u7684\u65b9\u6cd5\uff0c\u5176\u5de5\u4f5c\u539f\u7406\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6807\u51c6\u5316\u9884\u6d4b\u53d8\u91cf\u548c\u54cd\u5e94\u53d8\u91cf\u3002<\/span><\/li>\n<li>\u8ba1\u7b97<em style=\"color: #000000;\">p \u4e2a<\/em>\u539f\u59cb\u9884\u6d4b\u53d8\u91cf<span style=\"color: #000000;\">\u7684<em>M \u4e2a<\/em>\u7ebf\u6027\u7ec4\u5408\uff08\u79f0\u4e3a\u201cPLS \u5206\u91cf\u201d\uff09\uff0c<\/span><span style=\"color: #000000;\">\u8fd9\u4e9b\u7ec4\u5408\u89e3\u91ca\u4e86\u54cd\u5e94\u53d8\u91cf\u548c\u9884\u6d4b\u53d8\u91cf\u4e2d\u7684\u5927\u91cf\u53d8\u5316\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4f7f\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u4f7f\u7528 PLS \u5206\u91cf\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/k\u6298\u4ea4\u53c9\u9a8c\u8bc1\/\" target=\"_blank\" rel=\"noopener noreferrer\">k \u6298\u4ea4\u53c9\u9a8c\u8bc1<\/a>\u6765\u627e\u5230\u6a21\u578b\u4e2d\u4fdd\u7559\u7684 PLS \u7ec4\u4ef6\u7684\u6700\u4f73\u6570\u91cf\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u504f\u6700\u5c0f\u4e8c\u4e58\u6cd5\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c1\u6b65\uff1a\u5bfc\u5165\u5fc5\u8981\u7684\u5305<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u5bfc\u5165\u5728 Python \u4e2d\u6267\u884c\u504f\u6700\u5c0f\u4e8c\u4e58\u6cd5\u6240\u9700\u7684\u5305\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> scale \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> model_selection\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from <span style=\"color: #000000;\">sklearn. <span style=\"color: #3366ff;\">cross_decomposition<\/span> <span style=\"color: #008000;\">import<\/span> PLSRegression<\/span>\n<span style=\"color: #008000;\">from<\/span> <span style=\"color: #000000;\">sklearn<\/span> . <span style=\"color: #3366ff;\">metrics<\/span> <span style=\"color: #008000;\">import<\/span> <span style=\"color: #000000;\">mean_squared_error\n<\/span><\/span><\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c2\u6b65\uff1a\u52a0\u8f7d\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u540d\u4e3a<strong>mtcars<\/strong>\u7684\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b 33 \u8f86\u4e0d\u540c\u6c7d\u8f66\u7684\u4fe1\u606f\u3002\u6211\u4eec\u5c06\u4f7f\u7528<strong>hp<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff0c\u5e76\u4f7f\u7528\u4ee5\u4e0b\u53d8\u91cf\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u82f1\u91cc\/\u52a0\u4ed1<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5c55\u793a<\/span><\/li>\n<li><span style=\"color: #000000;\">\u62c9\u5c4e<\/span><\/li>\n<li><span style=\"color: #000000;\">\u91cd\u91cf<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5feb\u79d2<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u52a0\u8f7d\u548c\u663e\u793a\u6b64\u6570\u636e\u96c6\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data\n<\/span>data_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"disp\", \"drat\", \"wt\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data\n<\/span>data[0:6]\n\n\n        mpg disp drat wt qsec hp\n0 21.0 160.0 3.90 2.620 16.46 110\n1 21.0 160.0 3.90 2.875 17.02 110\n2 22.8 108.0 3.85 2.320 18.61 93\n3 21.4 258.0 3.08 3.215 19.44 110\n4 18.7 360.0 3.15 3.440 17.02 175\n5 18.1 225.0 2.76 3.460 20.22 105<\/strong><\/span><\/pre>\n<h3><strong>\u6b65\u9aa4 3\uff1a\u62df\u5408\u504f\u6700\u5c0f\u4e8c\u4e58\u6a21\u578b<\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u4f7f PLS \u6a21\u578b\u9002\u5408\u6b64\u6570\u636e\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c<\/span> <span style=\"color: #000000;\"><strong>cv = RepeatedKFold()<\/strong>\u544a\u8bc9 Python \u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/k\u6298\u4ea4\u53c9\u9a8c\u8bc1\/\" target=\"_blank\" rel=\"noopener noreferrer\">k \u6298\u4ea4\u53c9\u9a8c\u8bc1<\/a>\u6765\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002\u5bf9\u4e8e\u672c\u4f8b\uff0c\u6211\u4eec\u9009\u62e9 k = 10 \u6b21\uff0c\u91cd\u590d 3 \u6b21\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"disp\", \"drat\", \"wt\", \"qsec\"]]\ny = data[[\"hp\"]]\n\n<span style=\"color: #008080;\">#define cross-validation method\n<span style=\"color: #000000;\">cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\nmse = []\nn = <span style=\"color: #3366ff;\">len<\/span> (X)<\/span>\n\n# Calculate MSE with only the intercept\n<span style=\"color: #000000;\">score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (PLSRegression(n_components=1),<\/span>\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">ones<\/span> ((n,1)), y, cv=cv, scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()    \nmse. <span style=\"color: #3366ff;\">append<\/span> (score)<\/span>\n\n# Calculate MSE using cross-validation, adding one component at a time\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">for<\/span> i <span style=\"color: #008000;\">in<\/span> np. <span style=\"color: #3366ff;\">arange<\/span> (1, 6):\n    pls = PLSRegression(n_components=i)\n    score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (pls, scale(X), y, cv=cv,\n               scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()\n    mse. <span style=\"color: #3366ff;\">append<\/span> (score)<\/span>\n\n#plot test MSE vs. number of components\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">plot<\/span> (mse)\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #008000;\">Number of PLS Components<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #008000;\">MSE<\/span> ')\nplt. <span style=\"color: #3366ff;\">title<\/span> (' <span style=\"color: #008000;\">hp<\/span> ')<\/span>\n<\/span><\/strong><\/span><\/pre>\n<h3><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11985 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/svppython1.png\" alt=\"Python \u4ea4\u53c9\u9a8c\u8bc1\u56fe\u4e2d\u7684\u504f\u6700\u5c0f\u4e8c\u4e58\" width=\"405\" height=\"284\" srcset=\"\" sizes=\"auto, \"><\/h3>\n<p><span style=\"color: #000000;\">\u8be5\u56fe\u6cbf x \u8f74\u663e\u793a PLS \u5206\u91cf\u7684\u6570\u91cf\uff0c\u6cbf y \u8f74\u663e\u793a MSE\uff08\u5747\u65b9\u8bef\u5dee\uff09\u6d4b\u8bd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ece\u56fe\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u901a\u8fc7\u6dfb\u52a0\u4e24\u4e2a PLS \u7ec4\u4ef6\uff0c\u6d4b\u8bd5\u7684 MSE \u4f1a\u4e0b\u964d\uff0c\u4f46\u5f53\u6211\u4eec\u6dfb\u52a0\u4e24\u4e2a\u4ee5\u4e0a\u7684 PLS \u7ec4\u4ef6\u65f6\uff0cMSE \u5c31\u4f1a\u5f00\u59cb\u589e\u52a0\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u6700\u4f18\u6a21\u578b\u4ec5\u5305\u542b\u524d\u4e24\u4e2a PLS \u5206\u91cf\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 4 \u6b65\uff1a\u4f7f\u7528\u6700\u7ec8\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5177\u6709\u4e24\u4e2a PLS \u7ec4\u4ef6\u7684\u6700\u7ec8 PLS \u6a21\u578b\u6765\u5bf9\u65b0\u89c2\u6d4b\u503c\u8fdb\u884c\u9884\u6d4b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u539f\u59cb\u6570\u636e\u96c6\u62c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u5e76\u4f7f\u7528\u5177\u6709\u4e24\u4e2a PLS \u7ec4\u4ef6\u7684 PLS \u6a21\u578b\u5bf9\u6d4b\u8bd5\u96c6\u8fdb\u884c\u9884\u6d4b\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span><span style=\"color: #3366ff;\">X_train<\/span> <span style=\"color: #008000;\">,<\/span> <span style=\"color: #008000;\">_<\/span><span style=\"color: #008080;\">\n\n#calculate RMSE\n<span style=\"color: #000000;\">pls = PLSRegression(n_components=2)\npls. <span style=\"color: #3366ff;\">fit<\/span> (scale(X_train), y_train)<\/span>\n\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">sqrt<\/span> (mean_squared_error(y_test, pls. <span style=\"color: #3366ff;\">predict<\/span> (scale(X_test))))\n<\/span>\n<span style=\"color: #000000;\">29.9094\n<\/span><\/span><\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u770b\u5230\u6d4b\u8bd5\u7684 RMSE \u7ed3\u679c\u4e3a<strong>29.9094<\/strong> \u3002\u8fd9\u662f\u6d4b\u8bd5\u96c6\u89c2\u6d4b\u503c\u7684\u9884\u6d4b<em>hp<\/em>\u503c\u548c\u89c2\u5bdf\u5230\u7684<em>hp<\/em>\u503c\u4e4b\u95f4\u7684\u5e73\u5747\u504f\u5dee\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b64\u793a\u4f8b\u4e2d\u4f7f\u7528\u7684\u5b8c\u6574 Python \u4ee3\u7801\u53ef\u4ee5<a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/partial_least_squares.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u5728\u6b64\u5904<\/a>\u627e\u5230\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u673a\u5668\u5b66\u4e60\u4e2d\u6700\u5e38\u89c1\u7684\u95ee\u9898\u4e4b\u4e00\u662f\u591a\u91cd\u5171\u7ebf\u6027\u3002\u5f53\u6570\u636e\u96c6\u4e2d\u7684\u4e24\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u9ad8\u5ea6\u76f8\u5173\u65f6\uff0c\u5c31\u4f1a\u53d1\u751f\u8fd9\u79cd\u60c5\u51b5\u3002 \u53d1\u751f\u8fd9\u79cd [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1212","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u4e2d\u7684\u504f\u6700\u5c0f\u4e8c\u4e58\u6cd5\uff08\u4e00\u6b65\u4e00\u6b65\uff09 - 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