{"id":1128421,"date":"2025-01-08T20:20:22","date_gmt":"2025-01-08T12:20:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1128421.html"},"modified":"2025-01-08T20:20:25","modified_gmt":"2025-01-08T12:20:25","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e8%bf%9b%e8%a1%8c%e5%81%8f%e6%9c%80%e5%b0%8f%e4%ba%8c%e4%b9%98%e5%9b%9e%e5%bd%92-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1128421.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u8fdb\u884c\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25095125\/c7fd07d2-a836-446c-97a6-ff206ede3cba.webp\" alt=\"\u5982\u4f55\u5229\u7528python\u8fdb\u884c\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\" \/><\/p>\n<p><p> <strong>\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\uff08PLS\uff0cPartial Least Squares Regression\uff09<\/strong>\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5e7f\u6cdb\u7528\u4e8e\u89e3\u51b3\u9ad8\u7ef4\u6570\u636e\u4e2d\u591a\u91cd\u5171\u7ebf\u6027\u7684\u95ee\u9898\uff0c\u5728\u5316\u5b66\u3001\u7ecf\u6d4e\u5b66\u548c\u5de5\u7a0b\u7b49\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u5e94\u7528\u3002\u5229\u7528Python\u8fdb\u884cPLS\u56de\u5f52\uff0c<strong>\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a\u5bfc\u5165\u6570\u636e\u3001\u9884\u5904\u7406\u6570\u636e\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6a21\u578b<\/strong>\u3002\u5176\u4e2d\uff0c<strong>\u6570\u636e\u9884\u5904\u7406<\/strong>\u662f\u5173\u952e\u6b65\u9aa4\u4e4b\u4e00\uff0c\u56e0\u4e3aPLS\u56de\u5f52\u5bf9\u6570\u636e\u7684\u5c3a\u5ea6\u548c\u5206\u5e03\u654f\u611f\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5229\u7528Python\u8fdb\u884c\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\uff0c\u5e76\u7ed9\u51fa\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n<p><h2>\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h2>\n<\/p>\n<p><p>\u5728\u8fdb\u884cPLS\u56de\u5f52\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u4e00\u4e9b\u5fc5\u8981\u7684Python\u5e93\u3002\u8fd9\u4e9b\u5e93\u5305\u62ecNumPy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0cPandas\u7528\u4e8e\u6570\u636e\u5904\u7406\uff0cMatplotlib\u548cSeaborn\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0cScikit-learn\u7528\u4e8e<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u7684\u6784\u5efa\u548c\u8bc4\u4f30\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import pandas as pd<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<p>import seaborn as sns<\/p>\n<p>from sklearn.model_selection import tr<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n_test_split<\/p>\n<p>from sklearn.preprocessing import StandardScaler<\/p>\n<p>from sklearn.cross_decomposition import PLSRegression<\/p>\n<p>from sklearn.metrics import mean_squared_error, r2_score<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e8c\u3001\u5bfc\u5165\u4e0e\u9884\u5904\u7406\u6570\u636e<\/h2>\n<\/p>\n<p><p>\u6570\u636e\u9884\u5904\u7406\u662fPLS\u56de\u5f52\u7684\u5173\u952e\u6b65\u9aa4\u4e4b\u4e00\u3002\u6570\u636e\u9884\u5904\u7406\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7f3a\u5931\u503c\u5904\u7406\u3001\u7279\u5f81\u9009\u62e9\u548c\u6570\u636e\u6807\u51c6\u5316\u7b49\u6b65\u9aa4\u3002\u5728PLS\u56de\u5f52\u4e2d\uff0c\u6570\u636e\u6807\u51c6\u5316\u5c24\u4e3a\u91cd\u8981\uff0c\u56e0\u4e3aPLS\u56de\u5f52\u5bf9\u6570\u636e\u7684\u5c3a\u5ea6\u975e\u5e38\u654f\u611f\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8bfb\u53d6\u6570\u636e<\/p>\n<p>data = pd.read_csv(&#39;your_dataset.csv&#39;)<\/p>\n<h2><strong>\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n<p>print(data.info())<\/p>\n<h2><strong>\u68c0\u67e5\u662f\u5426\u6709\u7f3a\u5931\u503c<\/strong><\/h2>\n<p>print(data.isnull().sum())<\/p>\n<h2><strong>\u586b\u8865\u7f3a\u5931\u503c\u6216\u5220\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n<p>data = data.dropna()<\/p>\n<h2><strong>\u5206\u79bb\u7279\u5f81\u53d8\u91cf\u548c\u76ee\u6807\u53d8\u91cf<\/strong><\/h2>\n<p>X = data.drop(&#39;target&#39;, axis=1)<\/p>\n<p>y = data[&#39;target&#39;]<\/p>\n<h2><strong>\u6570\u636e\u6807\u51c6\u5316<\/strong><\/h2>\n<p>scaler = StandardScaler()<\/p>\n<p>X_scaled = scaler.fit_transform(X)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e09\u3001\u5206\u5272\u6570\u636e\u96c6<\/h2>\n<\/p>\n<p><p>\u5728\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5c06\u6570\u636e\u96c6\u5206\u5272\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002\u8fd9\u6837\u53ef\u4ee5\u66f4\u597d\u5730\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u5206\u5272\u6570\u636e\u96c6<\/p>\n<p>X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u56db\u3001\u8bad\u7ec3PLS\u56de\u5f52\u6a21\u578b<\/h2>\n<\/p>\n<p><p>\u5728Scikit-learn\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<code>PLSRegression<\/code>\u7c7b\u6765\u8bad\u7ec3PLS\u56de\u5f52\u6a21\u578b\u3002\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u5408\u9002\u7684\u6f5c\u5728\u53d8\u91cf\uff08components\uff09\u6570\u91cf\uff0c\u4ee5\u83b7\u5f97\u6700\u4f73\u7684\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8bad\u7ec3PLS\u56de\u5f52\u6a21\u578b<\/p>\n<p>pls = PLSRegression(n_components=2)<\/p>\n<p>pls.fit(X_train, y_train)<\/p>\n<h2><strong>\u9884\u6d4b<\/strong><\/h2>\n<p>y_train_pred = pls.predict(X_train)<\/p>\n<p>y_test_pred = pls.predict(X_test)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e94\u3001\u8bc4\u4f30\u6a21\u578b<\/h2>\n<\/p>\n<p><p>\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u662f\u673a\u5668\u5b66\u4e60\u8fc7\u7a0b\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u548c\u51b3\u5b9a\u7cfb\u6570\uff08R\u00b2\uff09\u6765\u8bc4\u4f30PLS\u56de\u5f52\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8bc4\u4f30\u8bad\u7ec3\u96c6\u6027\u80fd<\/p>\n<p>train_mse = mean_squared_error(y_train, y_train_pred)<\/p>\n<p>train_r2 = r2_score(y_train, y_train_pred)<\/p>\n<h2><strong>\u8bc4\u4f30\u6d4b\u8bd5\u96c6\u6027\u80fd<\/strong><\/h2>\n<p>test_mse = mean_squared_error(y_test, y_test_pred)<\/p>\n<p>test_r2 = r2_score(y_test, y_test_pred)<\/p>\n<p>print(f&#39;Training MSE: {train_mse}&#39;)<\/p>\n<p>print(f&#39;Training R\u00b2: {train_r2}&#39;)<\/p>\n<p>print(f&#39;Test MSE: {test_mse}&#39;)<\/p>\n<p>print(f&#39;Test R\u00b2: {test_r2}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u516d\u3001\u53ef\u89c6\u5316\u7ed3\u679c<\/h2>\n<\/p>\n<p><p>\u6570\u636e\u53ef\u89c6\u5316\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u76f4\u89c2\u5730\u4e86\u89e3\u6a21\u578b\u7684\u6027\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u771f\u5b9e\u503c\u4e0e\u9884\u6d4b\u503c\u7684\u5bf9\u6bd4\u56fe\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u7ed8\u5236\u771f\u5b9e\u503c\u4e0e\u9884\u6d4b\u503c\u7684\u5bf9\u6bd4\u56fe<\/p>\n<p>plt.figure(figsize=(10, 6))<\/p>\n<p>plt.scatter(y_test, y_test_pred, c=&#39;blue&#39;, marker=&#39;o&#39;, label=&#39;Test data&#39;)<\/p>\n<p>plt.xlabel(&#39;True values&#39;)<\/p>\n<p>plt.ylabel(&#39;Predicted values&#39;)<\/p>\n<p>plt.title(&#39;True vs Predicted values&#39;)<\/p>\n<p>plt.legend(loc=&#39;upper left&#39;)<\/p>\n<p>plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], &#39;k--&#39;, lw=2)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e03\u3001\u6a21\u578b\u8c03\u4f18<\/h2>\n<\/p>\n<p><p>\u4e3a\u4e86\u83b7\u5f97\u66f4\u597d\u7684\u6a21\u578b\u6027\u80fd\uff0c\u6211\u4eec\u53ef\u4ee5\u8c03\u4f18PLS\u56de\u5f52\u6a21\u578b\u7684\u53c2\u6570\u3002\u4e00\u4e2a\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u9009\u62e9\u6700\u4f73\u7684\u6f5c\u5728\u53d8\u91cf\u6570\u91cf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import GridSearchCV<\/p>\n<h2><strong>\u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n<p>param_grid = {&#39;n_components&#39;: np.arange(1, X_train.shape[1] + 1)}<\/p>\n<h2><strong>\u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n<p>pls_cv = GridSearchCV(PLSRegression(), param_grid, cv=5)<\/p>\n<p>pls_cv.fit(X_train, y_train)<\/p>\n<h2><strong>\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\n<p>best_n_components = pls_cv.best_params_[&#39;n_components&#39;]<\/p>\n<p>print(f&#39;Best number of components: {best_n_components}&#39;)<\/p>\n<h2><strong>\u4f7f\u7528\u6700\u4f73\u53c2\u6570\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<p>pls_best = PLSRegression(n_components=best_n_components)<\/p>\n<p>pls_best.fit(X_train, y_train)<\/p>\n<h2><strong>\u9884\u6d4b<\/strong><\/h2>\n<p>y_train_pred_best = pls_best.predict(X_train)<\/p>\n<p>y_test_pred_best = pls_best.predict(X_test)<\/p>\n<h2><strong>\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n<p>train_mse_best = mean_squared_error(y_train, y_train_pred_best)<\/p>\n<p>train_r2_best = r2_score(y_train, y_train_pred_best)<\/p>\n<p>test_mse_best = mean_squared_error(y_test, y_test_pred_best)<\/p>\n<p>test_r2_best = r2_score(y_test, y_test_pred_best)<\/p>\n<p>print(f&#39;Best Training MSE: {train_mse_best}&#39;)<\/p>\n<p>print(f&#39;Best Training R\u00b2: {train_r2_best}&#39;)<\/p>\n<p>print(f&#39;Best Test MSE: {test_mse_best}&#39;)<\/p>\n<p>print(f&#39;Best Test R\u00b2: {test_r2_best}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u516b\u3001\u603b\u7ed3<\/h2>\n<\/p>\n<p><p>\u5229\u7528Python\u8fdb\u884c\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\uff0c\u6211\u4eec\u53ef\u4ee5\u5206\u4e3a\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a\u5bfc\u5165\u6570\u636e\u3001\u9884\u5904\u7406\u6570\u636e\u3001\u5206\u5272\u6570\u636e\u96c6\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6a21\u578b\u3001\u53ef\u89c6\u5316\u7ed3\u679c\u548c\u6a21\u578b\u8c03\u4f18\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efa\u4e00\u4e2a\u6027\u80fd\u826f\u597d\u7684PLS\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u5e94\u7528\u4e8e\u5b9e\u9645\u95ee\u9898\u4e2d\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528PLS\u56de\u5f52\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u7684\u57fa\u672c\u6982\u5ff5\u662f\u4ec0\u4e48\uff1f<\/strong><br \/>\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\uff08PLSR\uff09\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u7528\u4e8e\u5efa\u7acb\u81ea\u53d8\u91cf\u548c\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u7279\u522b\u662f\u5728\u81ea\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u591a\u91cd\u5171\u7ebf\u6027\u7684\u60c5\u51b5\u4e0b\u3002\u5b83\u901a\u8fc7\u63d0\u53d6\u6f5c\u5728\u53d8\u91cf\u6765\u51cf\u5c11\u6570\u636e\u7684\u7ef4\u5ea6\uff0c\u540c\u65f6\u4fdd\u6301\u4e0e\u56e0\u53d8\u91cf\u7684\u76f8\u5173\u6027\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u3002PLSR\u5728\u5316\u5b66\u3001\u7ecf\u6d4e\u5b66\u548c\u751f\u7269\u7edf\u8ba1\u7b49\u9886\u57df\u5f97\u5230\u5e7f\u6cdb\u5e94\u7528\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u5b9e\u73b0\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ec<code>scikit-learn<\/code>\u548c<code>statsmodels<\/code>\u3002<code>scikit-learn<\/code>\u63d0\u4f9b\u4e86\u76f4\u63a5\u7684PLSR\u5b9e\u73b0\uff0c\u800c<code>statsmodels<\/code>\u5219\u5141\u8bb8\u8fdb\u884c\u66f4\u8be6\u7ec6\u7684\u7edf\u8ba1\u5206\u6790\u3002\u6b64\u5916\uff0c<code>numpy<\/code>\u548c<code>pandas<\/code>\u7528\u4e8e\u6570\u636e\u5904\u7406\u4e0e\u5206\u6790\uff0c\u8fd9\u4e9b\u5e93\u7ec4\u5408\u4f7f\u7528\u80fd\u6709\u6548\u5b9e\u73b0\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u7684\u5efa\u6a21\u548c\u8bc4\u4f30\u3002<\/p>\n<p><strong>\u5982\u4f55\u8bc4\u4f30\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u6a21\u578b\u7684\u6027\u80fd\uff1f<\/strong><br 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