{"id":998,"date":"2022-03-01T05:57:45","date_gmt":"2022-02-28T20:57:45","guid":{"rendered":"https:\/\/obgyn.jp\/?p=998"},"modified":"2022-03-13T19:51:50","modified_gmt":"2022-03-13T10:51:50","slug":"pca-fa","status":"publish","type":"post","link":"https:\/\/obgyn.jp\/?p=998","title":{"rendered":"Python \u306b\u3088\u308b\u4e3b\u6210\u5206\u5206\u6790\u3068\u56e0\u5b50\u5206\u6790"},"content":{"rendered":"\n<p><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.PCA.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\" title=\"sklearn.decomposition.PCA\">sklearn.decomposition.PCA<\/a> \u3092\u4f7f\u3044\u3001\u4e3b\u6210\u5206\u5206\u6790\u3092\u884c\u3046\u3002\u307e\u305f\u3001<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.FactorAnalysis.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\" title=\"sklearn.decomposition.FactorAnalysis\">sklearn.decomposition.FactorAnalysis<\/a> \u3092\u4f7f\u3044\u3001\u56e0\u5b50\u5206\u6790\u3092\u884c\u3046\u3002<\/p>\n\n\n\n<p>Python \u3092\u8d77\u52d5\u3057\u3001\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092 import \u3059\u308b\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA, FactorAnalysis<\/pre>\n\n\n\n<p>\u901a\u5e38\u306f Excel \u7b49\u3067\u4f5c\u6210\u3057\u305f CSV \u30d5\u30a1\u30a4\u30eb\u3092 pandas \u306b\u8aad\u307f\u8fbc\u3093\u3067\u5206\u6790\u3059\u308b\u3002\u3057\u304b\u3057\u3001\u3053\u3053\u3067\u306f\u4fbf\u5b9c\u7684\u306b\u4ee5\u4e0b\u306e\u7c21\u5358\u306a\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u3001data \u3068\u3044\u3046\u5909\u6570\u306b\u683c\u7d0d\u3057\u3066\u304a\u304f\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">data = pd.DataFrame({\"\u624b\u8853\u6642\u9593\":[60,50,100,90,30,40,80,90], \"\u51fa\u8840\u91cf\":[15,100,90,20,18,20,120,90], \"\u7d50\u7d2e\u5931\u6557\u56de\u6570\":[0,1,2,3,2,1,5,1]})<\/pre>\n\n\n\n<p>data \u306e\u5185\u5bb9\u3092\u78ba\u8a8d\u3059\u308b\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">data<\/pre>\n\n\n\n<p>data \u3092\u6a19\u6e96\u5316\u3059\u308b\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">sc = StandardScaler()\nsc.fit(data)\ndata_s = sc.transform(data)<\/pre>\n\n\n\n<p><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.PCA.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\" title=\"sklearn.decomposition.PCA\">sklearn.decomposition.PCA<\/a> \u3092\u4f7f\u3044\u3001\u4e3b\u6210\u5206\u5206\u6790\u3092\u884c\u3046\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">pca = PCA()\npca.fit(data_s)\ndata_t = pca.transform(data_s)\n \ndata_t  #\u4e3b\u6210\u5206\u5f97\u70b9\n\npca.explained_variance_ratio_  #\u5bc4\u4e0e\u7387\nnp.cumsum(pca.explained_variance_ratio_)  #\u7d2f\u7a4d\u5bc4\u4e0e\u7387\n\npca.explained_variance_ #\u56fa\u6709\u5024\npca.components_ #\u56fa\u6709\u30d9\u30af\u30c8\u30eb<\/pre>\n\n\n\n<p>\u7b2c1\u4e3b\u6210\u5206\uff08PC1\uff09\u3068\u7b2c2\u4e3b\u6210\u5206\uff08PC2\uff09\u3067\u6563\u5e03\u56f3\u3092\u63cf\u304d\u305f\u3044\u5834\u5408\u306f matplotlib \u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">from matplotlib import pyplot as plt\nx = data_t[:, 0] #\u7b2c1\u4e3b\u6210\u5206\u3092\u62bd\u51fa\ny = data_t[:, 1] #\u7b2c2\u4e3b\u6210\u5206\u3092\u62bd\u51fa\nplt.figure()\nplt.scatter(x, y)\nplt.grid()\nplt.xlabel(\"PC1\")\nplt.ylabel(\"PC2\")\nplt.show()<\/pre>\n\n\n\n<p><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.FactorAnalysis.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\" title=\"sklearn.decomposition.FactorAnalysis\">sklearn.decomposition.FactorAnalysis<\/a> \u3092\u4f7f\u3044\u3001\u56e0\u5b50\u5206\u6790\u3092\u884c\u3046\u3002\u3053\u3053\u3067\u306f\u56e0\u5b50\u6570\u306f 1\u3001varimax \u56de\u8ee2\u3092\u9078\u629e\u3057\u3066\u3044\u308b\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">FA = FactorAnalysis(n_components = 1, rotation = \"varimax\") \nFA.fit_transform(data_s) #\u56e0\u5b50\u5f97\u70b9\nFA.components_ #\u56e0\u5b50\u8ca0\u8377\u91cf<\/pre>\n","protected":false},"excerpt":{"rendered":"<p>sklearn.decomposition.PCA \u3092\u4f7f\u3044\u3001\u4e3b\u6210\u5206\u5206\u6790\u3092\u884c\u3046\u3002\u307e\u305f\u3001sklearn.decomposition.FactorAnalysis \u3092\u4f7f\u3044\u3001\u56e0\u5b50\u5206\u6790\u3092\u884c\u3046\u3002 Python \u3092\u8d77\u52d5\u3057\u3001\u5fc5\u8981\u306a\u30e9\u30a4\u30d6 &hellip; <a href=\"https:\/\/obgyn.jp\/?p=998\" class=\"more-link\">\u7d9a\u304d\u3092\u8aad\u3080 <span class=\"screen-reader-text\">Python \u306b\u3088\u308b\u4e3b\u6210\u5206\u5206\u6790\u3068\u56e0\u5b50\u5206\u6790<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[18,36],"class_list":["post-998","post","type-post","status-publish","format-standard","hentry","category-data-science","tag-multivariate","tag-python"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/obgyn.jp\/index.php?rest_route=\/wp\/v2\/posts\/998","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/obgyn.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/obgyn.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/obgyn.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/obgyn.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=998"}],"version-history":[{"count":17,"href":"https:\/\/obgyn.jp\/index.php?rest_route=\/wp\/v2\/posts\/998\/revisions"}],"predecessor-version":[{"id":1015,"href":"https:\/\/obgyn.jp\/index.php?rest_route=\/wp\/v2\/posts\/998\/revisions\/1015"}],"wp:attachment":[{"href":"https:\/\/obgyn.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=998"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/obgyn.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=998"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/obgyn.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=998"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}