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\u652f\u6301\u7528\u6237\u9009\u62e9\u76ee\u6807\u7535\u5f71&#xff0c;\u901a\u8fc7KNNWithZScore\u7b97\u6cd5\u5206\u6790\u7528\u6237\u504f\u597d\u4e0e\u76f8\u4f3c\u7528\u6237\u6570\u636e&#xff0c;\u751f\u6210Top10\u63a8\u8350\u5217\u8868\u5e76\u4ee5\u8868\u683c\u5c55\u793a\u7535\u5f71\u4fe1\u606f&#xff0c;\u5e2e\u52a9\u7528\u6237\u5feb\u901f\u83b7\u53d6\u5339\u914d\u504f\u597d\u7684\u5185\u5bb9\u3002<\/p>\n<\/li>\n<li>\n<p>\u6570\u636e\u53ef\u89c6\u5316\u5c55\u793a\u6a21\u5757 \u5305\u542b\u4e24\u5927\u53ef\u89c6\u5316\u5927\u5c4f&#xff1a;\u7535\u5f71\u6570\u636e\u5927\u5c4f\u4ee5\u591a\u677f\u5757\u5448\u73b0\u7c7b\u578b\u6570\u91cf\u3001\u56fd\u5bb6\u5206\u5e03\u3001\u5e74\u5ea6\u8d8b\u52bf\u7b49\u6838\u5fc3\u6307\u6807&#xff1b;\u8bc4\u8bba\u6570\u636e\u5927\u5c4f\u901a\u8fc7\u56fe\u8868\u4e0e\u8bcd\u4e91&#xff0c;\u5c55\u793a\u8bc4\u8bba\u7528\u6237\u65f6\u95f4\u5206\u5e03\u3001\u5185\u5bb9\u5173\u952e\u8bcd\u53ca\u70ed\u95e8\u7535\u5f71\u5173\u8054\u5ea6&#xff0c;\u76f4\u89c2\u5448\u73b0\u6570\u636e\u7279\u5f81\u3002<\/p>\n<\/li>\n<li>\n<p>\u7528\u6237\u89d2\u8272\u4e0e\u529f\u80fd\u5206\u914d\u6a21\u5757 \u652f\u6301\u4e09\u7ea7\u89d2\u8272\u767b\u5f55&#xff1a;\u666e\u901a\u7528\u6237\u53ef\u6d4f\u89c8\u7535\u5f71\u3001\u63a5\u6536\u63a8\u8350\u3001\u7ef4\u62a4\u4e2a\u4eba\u4fe1\u606f&#xff1b;\u7ba1\u7406\u5458\u8d1f\u8d23\u7535\u5f71\u4e0e\u7528\u6237\u4fe1\u606f\u7684\u589e\u5220\u6539\u67e5&#xff1b;\u540e\u53f0\u7ba1\u7406\u5458\u7ba1\u7406\u7cfb\u7edf\u6570\u636e\u3001\u76d1\u63a7\u722c\u866b\u4e0e\u6a21\u578b\u53c2\u6570&#xff0c;\u5b9e\u73b0\u6743\u9650\u5206\u5c42\u4e0e\u529f\u80fd\u6309\u9700\u5206\u914d\u3002<\/p>\n<\/li>\n<li>\n<p>\u7535\u5f71\u4fe1\u606f\u7ba1\u7406\u6a21\u5757 \u63d0\u4f9b\u4e0b\u62c9\u7b5b\u9009\u67e5\u8be2\u529f\u80fd&#xff0c;\u4ee5\u8868\u683c\u5c55\u793a\u7535\u5f71\u591a\u7c7b\u4fe1\u606f&#xff0c;\u6bcf\u6761\u6570\u636e\u914d\u5907\u64cd\u4f5c\u6309\u94ae&#xff0c;\u7ba1\u7406\u5458\u901a\u8fc7\u8be5\u6a21\u5757\u5b9e\u73b0\u7535\u5f71\u4fe1\u606f\u7684\u66f4\u65b0\u4e0e\u7ef4\u62a4&#xff0c;\u4fdd\u969c\u6570\u636e\u7684\u65f6\u6548\u6027\u4e0e\u6709\u5e8f\u6027\u3002<\/p>\n<\/li>\n<li>\n<p>\u7528\u6237\u4fe1\u606f\u7ba1\u7406\u6a21\u5757 \u652f\u6301\u7528\u6237\u4fe1\u606f\u7684\u7b5b\u9009\u67e5\u8be2&#xff0c;\u8868\u683c\u5c55\u793a\u7528\u6237\u540d\u3001\u7c7b\u578b\u7b49\u5185\u5bb9\u5e76\u914d\u5907\u64cd\u4f5c\u6309\u94ae&#xff0c;\u7ba1\u7406\u5458\u53ef\u901a\u8fc7\u8be5\u6a21\u5757\u7ef4\u62a4\u7528\u6237\u8d26\u53f7&#xff0c;\u786e\u4fdd\u7cfb\u7edf\u7528\u6237\u7ba1\u7406\u7684\u89c4\u8303\u3002<\/p>\n<\/li>\n<li>\n<p>\u540e\u53f0\u6570\u636e\u7ba1\u7406\u6a21\u5757 \u63d0\u4f9b\u591a\u6807\u7b7e\u9875\u5207\u6362&#xff08;\u8986\u76d6\u7535\u5f71\u3001\u7968\u623f\u7b49\u6570\u636e\u5206\u7c7b&#xff09;&#xff0c;\u652f\u6301\u641c\u7d22\u3001\u6279\u91cf\u64cd\u4f5c\u4e0e\u5206\u9875\u6d4f\u89c8&#xff0c;\u4ee5\u8868\u683c\u5c55\u793a\u6570\u636e\u8be6\u60c5\u5e76\u914d\u5907\u7f16\u8f91\u6309\u94ae&#xff0c;\u540e\u53f0\u7ba1\u7406\u5458\u901a\u8fc7\u8be5\u6a21\u5757\u5b9e\u73b0\u6838\u5fc3\u6570\u636e\u7684\u96c6\u4e2d\u7ba1\u7406&#xff0c;\u652f\u6491\u524d\u7aef\u529f\u80fd\u7a33\u5b9a\u8fd0\u884c\u3002<\/p>\n<\/li>\n<li>\n<p>\u6ce8\u518c\u767b\u5f55\u6a21\u5757 \u4f5c\u4e3a\u7cfb\u7edf\u8bbf\u95ee\u5165\u53e3&#xff0c;\u63d0\u4f9b\u8d26\u53f7\u3001\u5bc6\u7801\u8f93\u5165\u6846\u4e0e\u767b\u5f55\u6309\u94ae&#xff0c;\u540c\u65f6\u914d\u5907\u6ce8\u518c\u5165\u53e3&#xff0c;\u7528\u6237\u5b8c\u6210\u8eab\u4efd\u9a8c\u8bc1\u540e\u8fdb\u5165\u5bf9\u5e94\u89d2\u8272\u754c\u9762&#xff0c;\u5b9e\u73b0\u6743\u9650\u533a\u5206\u4e0e\u5b89\u5168\u8bbf\u95ee\u3002<\/p>\n<\/li>\n<p>\u4e09\u3001\u9879\u76ee\u603b\u7ed3 \u672c\u9879\u76ee\u662f\u96c6\u201c\u6570\u636e\u91c7\u96c6-\u5206\u6790-\u9884\u6d4b-\u63a8\u8350-\u7ba1\u7406\u201d\u4e8e\u4e00\u4f53\u7684\u7535\u5f71\u7cfb\u7edf&#xff0c;\u901a\u8fc7\u591a\u6280\u672f\u6574\u5408\u89e3\u51b3\u4e86\u7528\u6237\u9009\u7247\u96be\u3001\u884c\u4e1a\u7968\u623f\u9884\u6d4b\u76f2\u76ee\u7b49\u75db\u70b9\u3002\u7cfb\u7edf\u4ee5\u6570\u636e\u4e3a\u57fa\u7840\u3001\u7b97\u6cd5\u4e3a\u6838\u5fc3\u3001\u53ef\u89c6\u5316\u4e0e\u5206\u5c42\u7ba1\u7406\u4e3a\u652f\u6491&#xff0c;\u65e2\u6ee1\u8db3\u666e\u901a\u7528\u6237\u7684\u4e2a\u6027\u5316\u89c2\u5f71\u9700\u6c42&#xff0c;\u53c8\u4e3a\u7535\u5f71\u884c\u4e1a\u63d0\u4f9b\u6570\u636e\u9a71\u52a8\u7684\u51b3\u7b56\u5de5\u5177&#xff0c;\u5b9e\u73b0\u4e86\u6280\u672f\u6df1\u5ea6\u4e0e\u5b9e\u9645\u5e94\u7528\u4ef7\u503c\u7684\u7edf\u4e00\u3002<\/p>\n<h3>4\u3001\u6838\u5fc3\u4ee3\u7801<\/h3>\n<p><span class=\"token keyword\">import<\/span> re<br \/>\n<span class=\"token keyword\">import<\/span> os<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<br \/>\n<span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">import<\/span> pandas <span class=\"token keyword\">as<\/span> pd<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>ensemble <span class=\"token keyword\">import<\/span> RandomForestRegressor<span class=\"token punctuation\">,<\/span> GradientBoostingRegressor<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>metrics <span class=\"token keyword\">import<\/span> make_scorer<span class=\"token punctuation\">,<\/span> mean_squared_error<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>metrics <span class=\"token keyword\">import<\/span> r2_score<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> GridSearchCV<span class=\"token punctuation\">,<\/span> train_test_split<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> KFold<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeRegressor<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>linear_model <span class=\"token keyword\">import<\/span> LinearRegression <span class=\"token keyword\">as<\/span> LR<span class=\"token punctuation\">,<\/span> Lasso<br \/>\n<span class=\"token keyword\">import<\/span> joblib<br \/>\n<span class=\"token keyword\">import<\/span> seaborn <span class=\"token keyword\">as<\/span> sns<br \/>\nmodel_save_path <span class=\"token operator\">&#061;<\/span> <span class=\"token string\">r&#039;.\/app\/dataset\/testModel\/&#039;<\/span><br \/>\n<span class=\"token keyword\">if<\/span> <span class=\"token keyword\">not<\/span> os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>exists<span class=\"token punctuation\">(<\/span>model_save_path<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    os<span class=\"token punctuation\">.<\/span>makedirs<span class=\"token punctuation\">(<\/span>model_save_path<span class=\"token punctuation\">)<\/span><br \/>\ndata <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>read_csv<span class=\"token punctuation\">(<\/span><span class=\"token string\">r&#034;.\/app\/dataset\/ana_result\/piaofang_info.csv&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndata <span class=\"token operator\">&#061;<\/span> data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">7<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">9<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">11<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><br \/>\nX <span class=\"token operator\">&#061;<\/span> data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span><span class=\"token number\">7<\/span><span class=\"token punctuation\">]<\/span><br \/>\ny <span class=\"token operator\">&#061;<\/span> data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">7<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">apply<\/span><span class=\"token punctuation\">(<\/span><span class=\"token keyword\">lambda<\/span> x<span class=\"token punctuation\">:<\/span> x <span class=\"token operator\">\/<\/span> <span class=\"token number\">10000<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token comment\"># \u6807\u7b7e\u7ecf\u8fc7 log1p \u8f6c\u6362&#xff0c;\u4f7f\u5176\u66f4\u504f\u5411\u4e8e\u6b63\u6001\u5206\u5e03<\/span><br \/>\ny <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>log1p<span class=\"token punctuation\">(<\/span>y<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6570\u636e\u96c6\u5212\u5206<\/span><br \/>\ntrain_X<span class=\"token punctuation\">,<\/span> test_X<span class=\"token punctuation\">,<\/span> train_y<span class=\"token punctuation\">,<\/span> test_y <span class=\"token operator\">&#061;<\/span> train_test_split<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> test_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>oof_df <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>DataFrame<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntest_oof_df <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>DataFrame<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">performance_metric<\/span><span class=\"token punctuation\">(<\/span>y_true<span class=\"token punctuation\">,<\/span> y_predict<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token triple-quoted-string string\">&#034;&#034;&#034; Calculates and returns the performance score between<br \/>\n        true and predicted values based on the metric chosen. &#034;&#034;&#034;<\/span><\/p>\n<p>    <span class=\"token comment\"># \u8ba1\u7b97 &#039;y_true&#039; \u4e0e &#039;y_predict&#039; \u7684r2\u503c<\/span><br \/>\n    score <span class=\"token operator\">&#061;<\/span> r2_score<span class=\"token punctuation\">(<\/span>y_true<span class=\"token punctuation\">,<\/span> y_predict<span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># \u8fd4\u56de\u8fd9\u4e00\u5206\u6570<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> score<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">fit_dtr_model<\/span><span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    cross_validator <span class=\"token operator\">&#061;<\/span> KFold<span class=\"token punctuation\">(<\/span>n_splits<span class=\"token operator\">&#061;<\/span><span class=\"token number\">5<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    regressor <span class=\"token operator\">&#061;<\/span> DecisionTreeRegressor<span class=\"token punctuation\">(<\/span>random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># Create a dictionary for the parameter &#039;max_depth&#039; with a range from 1 to 10<\/span><br \/>\n    params <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><span class=\"token string\">&#039;max_depth&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span>i <span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">11<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">}<\/span><br \/>\n    <span class=\"token comment\"># Transform &#039;performance_metric&#039; into a scoring function using &#039;make_scorer&#039;<\/span><br \/>\n    scoring_fnc <span class=\"token operator\">&#061;<\/span> make_scorer<span class=\"token punctuation\">(<\/span>performance_metric<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># Create the grid search cv object &#8211;&gt; GridSearchCV()<\/span><br \/>\n    grid <span class=\"token operator\">&#061;<\/span> GridSearchCV<span class=\"token punctuation\">(<\/span>regressor<span class=\"token punctuation\">,<\/span> params<span class=\"token punctuation\">,<\/span> scoring<span class=\"token operator\">&#061;<\/span>scoring_fnc<span class=\"token punctuation\">,<\/span> cv<span class=\"token operator\">&#061;<\/span>cross_validator<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># Fit the grid search object to the data to compute the optimal model<\/span><br \/>\n    grid <span class=\"token operator\">&#061;<\/span> grid<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><br \/>\n    dtr_max_depth <span class=\"token operator\">&#061;<\/span> grid<span class=\"token punctuation\">.<\/span>best_estimator_<span class=\"token punctuation\">.<\/span>get_params<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;max_depth&#039;<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p>    <span class=\"token comment\"># Return the optimal model after fitting the data<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> dtr_max_depth<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">fit_decision_tree_model_forcast<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u8fdb\u884c\u51b3\u7b56\u6811\u9884\u6d4b\u6a21\u578b\u7684\u8bad\u7ec3<\/span><br \/>\n    dtr_max_depth <span class=\"token operator\">&#061;<\/span> fit_dtr_model<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><br \/>\n    dtr_regressor <span class=\"token operator\">&#061;<\/span> DecisionTreeRegressor<span class=\"token punctuation\">(<\/span>max_depth<span class=\"token operator\">&#061;<\/span>dtr_max_depth<span class=\"token punctuation\">)<\/span><br \/>\n    dtr_regressor<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><br \/>\n    pred_y <span class=\"token operator\">&#061;<\/span> dtr_regressor<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>test_X<span class=\"token punctuation\">)<\/span><br \/>\n    test_oof_df<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;dtr&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> pred_y<br \/>\n    r2_score <span class=\"token operator\">&#061;<\/span> performance_metric<span class=\"token punctuation\">(<\/span>test_y<span class=\"token punctuation\">,<\/span> pred_y<span class=\"token punctuation\">)<\/span><br \/>\n    rmse_score <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>sqrt<span class=\"token punctuation\">(<\/span>mean_squared_error<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u51b3\u7b56\u6811\u56de\u5f52\u6a21\u578b\u8bc4\u4ef7\u6307\u6807\u4e3a&#xff1a;&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;The R2 score is &#034;<\/span><span class=\"token punctuation\">,<\/span> r2_score<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u5747\u65b9\u5dee&#039;<\/span><span class=\"token punctuation\">,<\/span> rmse_score<span class=\"token punctuation\">)<\/span><\/p>\n<p>    joblib<span class=\"token punctuation\">.<\/span>dump<span class=\"token punctuation\">(<\/span>dtr_regressor<span class=\"token punctuation\">,<\/span> model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;dtr_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> rmse_score<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">fit_lasso_model_forcast<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u8fdb\u884cLasso\u9884\u6d4b\u6a21\u578b\u7684\u8bad\u7ec3<\/span><br \/>\n    lasso_regressor <span class=\"token operator\">&#061;<\/span> Lasso<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    lasso_regressor<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><br \/>\n    pred_y <span class=\"token operator\">&#061;<\/span> lasso_regressor<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>test_X<span class=\"token punctuation\">)<\/span><br \/>\n    test_oof_df<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;lasso&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> pred_y<br \/>\n    r2_score <span class=\"token operator\">&#061;<\/span> performance_metric<span class=\"token punctuation\">(<\/span>test_y<span class=\"token punctuation\">,<\/span> pred_y<span class=\"token punctuation\">)<\/span><br \/>\n    rmse_score <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>sqrt<span class=\"token punctuation\">(<\/span>mean_squared_error<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Lasso\u56de\u5f52\u6a21\u578b\u8bc4\u4ef7\u6307\u6807\u4e3a&#xff1a;&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;The R2 score is &#034;<\/span><span class=\"token punctuation\">,<\/span> r2_score<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u5747\u65b9\u5dee&#039;<\/span><span class=\"token punctuation\">,<\/span> rmse_score<span class=\"token punctuation\">)<\/span><\/p>\n<p>    joblib<span class=\"token punctuation\">.<\/span>dump<span class=\"token punctuation\">(<\/span>lasso_regressor<span class=\"token punctuation\">,<\/span> model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;lasso_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> rmse_score<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">fit_random_forest_regression_model<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    rf_model <span class=\"token operator\">&#061;<\/span> RandomForestRegressor<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    rf_model<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><br \/>\n    pred_y <span class=\"token operator\">&#061;<\/span> rf_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>test_X<span class=\"token punctuation\">)<\/span><br \/>\n    test_oof_df<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;rf&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> pred_y<br \/>\n    r2_score <span class=\"token operator\">&#061;<\/span> performance_metric<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><br \/>\n    rmse_score <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>sqrt<span class=\"token punctuation\">(<\/span>mean_squared_error<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u968f\u673a\u68ee\u6797\u6a21\u578b\u8bc4\u4ef7\u6307\u6807\u4e3a&#xff1a;&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;The R2 score is &#034;<\/span><span class=\"token punctuation\">,<\/span> r2_score<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u5747\u65b9\u5dee&#039;<\/span><span class=\"token punctuation\">,<\/span> rmse_score<span class=\"token punctuation\">)<\/span><br \/>\n    joblib<span class=\"token punctuation\">.<\/span>dump<span class=\"token punctuation\">(<\/span>rf_model<span class=\"token punctuation\">,<\/span> model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;rf_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> rmse_score<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">fit_gdbt_model<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    gdbt_model <span class=\"token operator\">&#061;<\/span> GradientBoostingRegressor<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    gdbt_model<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><br \/>\n    pred_y <span class=\"token operator\">&#061;<\/span> gdbt_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>test_X<span class=\"token punctuation\">)<\/span><br \/>\n    test_oof_df<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;gdbt&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> pred_y<br \/>\n    r2_score <span class=\"token operator\">&#061;<\/span> performance_metric<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><br \/>\n    rmse_score <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>sqrt<span class=\"token punctuation\">(<\/span>mean_squared_error<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;GDBT\u6a21\u578b\u8bc4\u4ef7\u6307\u6807\u4e3a&#xff1a;&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;The R2 score is &#034;<\/span><span class=\"token punctuation\">,<\/span> r2_score<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u5747\u65b9\u5dee&#039;<\/span><span class=\"token punctuation\">,<\/span> rmse_score<span class=\"token punctuation\">)<\/span><br \/>\n    joblib<span class=\"token punctuation\">.<\/span>dump<span class=\"token punctuation\">(<\/span>gdbt_model<span class=\"token punctuation\">,<\/span> model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;gdbt_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> rmse_score<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">fit_stacking_model<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    lr_model <span class=\"token operator\">&#061;<\/span> LR<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    lr_model<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>test_oof_df<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><br \/>\n    pred_y <span class=\"token operator\">&#061;<\/span> lr_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>test_oof_df<span class=\"token punctuation\">)<\/span><br \/>\n    r2_score <span class=\"token operator\">&#061;<\/span> performance_metric<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><br \/>\n    rmse_score <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>sqrt<span class=\"token punctuation\">(<\/span>mean_squared_error<span class=\"token punctuation\">(<\/span>pred_y<span class=\"token punctuation\">,<\/span> test_y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;Staking\u6a21\u578b\u8bc4\u4ef7\u6307\u6807\u4e3a&#xff1a;&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;The R2 score is &#034;<\/span><span class=\"token punctuation\">,<\/span> r2_score<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u5747\u65b9\u5dee&#039;<\/span><span class=\"token punctuation\">,<\/span> rmse_score<span class=\"token punctuation\">)<\/span><br \/>\n    joblib<span class=\"token punctuation\">.<\/span>dump<span class=\"token punctuation\">(<\/span>lr_model<span class=\"token punctuation\">,<\/span> model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;stacking_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> rmse_score<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">forcast_piaofang<\/span><span class=\"token punctuation\">(<\/span>para<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    para <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>DataFrame<span class=\"token punctuation\">(<\/span>para<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u52a0\u8f7d\u51b3\u7b56\u6811\u9884\u6d4b\u6a21\u578b<\/span><br \/>\n    dtr_model <span class=\"token operator\">&#061;<\/span> joblib<span class=\"token punctuation\">.<\/span>load<span class=\"token punctuation\">(<\/span>model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;dtr_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    dtr_pred <span class=\"token operator\">&#061;<\/span> dtr_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>para<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u51b3\u7b56\u6811\u9884\u6d4b\u7968\u623f%s\u4e07&#034;<\/span> <span class=\"token operator\">%<\/span> np<span class=\"token punctuation\">.<\/span>expm1<span class=\"token punctuation\">(<\/span>dtr_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u52a0\u8f7dLasso\u9884\u6d4b\u6a21\u578b<\/span><br \/>\n    lasso_model <span class=\"token operator\">&#061;<\/span> joblib<span class=\"token punctuation\">.<\/span>load<span class=\"token punctuation\">(<\/span>model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;lasso_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    lasso_pred <span class=\"token operator\">&#061;<\/span> lasso_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>para<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;Lasso\u9884\u6d4b\u7968\u623f%s\u4e07&#034;<\/span> <span class=\"token operator\">%<\/span> np<span class=\"token punctuation\">.<\/span>expm1<span class=\"token punctuation\">(<\/span>lasso_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># # \u52a0\u8f7d\u968f\u673a\u68ee\u6797\u9884\u6d4b\u6a21\u578b<\/span><br \/>\n    rf_model <span class=\"token operator\">&#061;<\/span> joblib<span class=\"token punctuation\">.<\/span>load<span class=\"token punctuation\">(<\/span>model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;rf_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    rf_pred <span class=\"token operator\">&#061;<\/span> rf_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>para<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u968f\u673a\u68ee\u6797\u9884\u6d4b\u7968\u623f%s\u4e07&#034;<\/span> <span class=\"token operator\">%<\/span> np<span class=\"token punctuation\">.<\/span>expm1<span class=\"token punctuation\">(<\/span>rf_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u52a0\u8f7dGDBT\u9884\u6d4b\u6a21\u578b<\/span><br \/>\n    gdbt_model <span class=\"token operator\">&#061;<\/span> joblib<span class=\"token punctuation\">.<\/span>load<span class=\"token punctuation\">(<\/span>model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;gdbt_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    gdbt_pred <span class=\"token operator\">&#061;<\/span> gdbt_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>para<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;GDBT\u9884\u6d4b\u7968\u623f%s\u4e07&#034;<\/span> <span class=\"token operator\">%<\/span> np<span class=\"token punctuation\">.<\/span>expm1<span class=\"token punctuation\">(<\/span>gdbt_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># return [dtr_pred, lr_pred]<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">[<\/span>dtr_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> lasso_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> rf_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> gdbt_pred<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p>\u6838\u5fc3\u4ee3\u7801\u5757\u4e8c&#xff1a;<\/p>\n<p><span class=\"token comment\"># \u7528\u4e8e\u8bad\u7ec3\u591a\u4e2a\u6a21\u578b\u5e76\u8ba1\u7b97\u5b83\u4eec\u7684 RMSE&#xff08;\u5747\u65b9\u6839\u8bef\u5dee&#xff09;\u5206\u6570&#xff0c;\u5e76\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u4e00\u4e2a CSV \u6587\u4ef6\u4e2d\u3002<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">train_model<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    dtr_rmse <span class=\"token operator\">&#061;<\/span> fit_decision_tree_model_forcast<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u51b3\u7b56\u6811<\/span><br \/>\n    lasso_rmse <span class=\"token operator\">&#061;<\/span> fit_lasso_model_forcast<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>           <span class=\"token comment\"># Lasso<\/span><br \/>\n    rf_rmse <span class=\"token operator\">&#061;<\/span> fit_random_forest_regression_model<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>   <span class=\"token comment\"># \u968f\u673a\u68ee\u6797<\/span><br \/>\n    gdbt_rmse <span class=\"token operator\">&#061;<\/span> fit_gdbt_model<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>         <span class=\"token comment\"># GDBT<\/span><br \/>\n    lr_rmse <span class=\"token operator\">&#061;<\/span> fit_stacking_model<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>      <span class=\"token comment\"># \u5c06\u8fd4\u56de\u7684\u5806\u53e0\u6a21\u578b\u7684 RMSE \u5206\u6570\u8d4b\u503c\u7ed9\u53d8\u91cflr_rmse<\/span><br \/>\n    rmse_result <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>DataFrame<span class=\"token punctuation\">(<\/span>index<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#034;\u51b3\u7b56\u6811&#034;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#034;Lasso&#034;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#034;\u968f\u673a\u68ee\u6797&#034;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#034;GDBT&#034;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#034;Stacking&#034;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    rmse_result<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;rmse_score&#039;<\/span><span class=\"token punctuation\">]<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span>dtr_rmse<span class=\"token punctuation\">,<\/span> lasso_rmse<span class=\"token punctuation\">,<\/span> rf_rmse<span class=\"token punctuation\">,<\/span> gdbt_rmse<span class=\"token punctuation\">,<\/span> lr_rmse<span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u5c06\u4e4b\u524d\u8ba1\u7b97\u5f97\u5230\u7684\u5404\u4e2a\u6a21\u578b\u7684 RMSE \u5206\u6570\u6dfb\u52a0\u5230rmse_result\u6570\u636e\u5e27\u4e2d\u7684rmse_score\u5217\u4e2d\u3002<\/span><br \/>\n    rmse_result<span class=\"token punctuation\">.<\/span>to_csv<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;..\/dataset\/testModel\/rmse_result.csv&#034;<\/span><span class=\"token punctuation\">,<\/span> encoding<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;utf-8&#039;<\/span><span class=\"token punctuation\">,<\/span> index<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">False<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\">#\u5c06rmse_result\u6570\u636e\u5e27\u4fdd\u5b58\u4e3a\u4e00\u4e2a CSV \u6587\u4ef6<\/span><\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">test_model<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># 1965, 12, 8.9, 1, 3, 29, 132<\/span><br \/>\n    <span class=\"token comment\"># 1295124,\u8f9b\u5fb7\u52d2\u7684\u540d\u5355,1993,11,9.6,3,&#034;[&#039;\u5267\u60c5&#039;, &#039;\u5386\u53f2&#039;, &#039;\u6218\u4e89&#039;]&#034;,1,[&#039;\u7f8e\u56fd&#039;],48,195,322161245<\/span><br \/>\n    <span class=\"token comment\"># 10876425,\u5370\u5f0f\u82f1\u8bed,2023,02,8.1,3,&#034;[&#039;\u5267\u60c5&#039;, &#039;\u559c\u5267&#039;, &#039;\u5bb6\u5ead&#039;]&#034;,1,[&#039;\u5370\u5ea6&#039;],13,133,10299150<\/span><br \/>\n    <span class=\"token comment\"># 35267208,\u6d41\u6d6a\u5730\u74032,2023,01,8.4,3,&#034;[&#039;\u79d1\u5e7b&#039;, &#039;\u5192\u9669&#039;, &#039;\u707e\u96be&#039;]&#034;,1,[&#039;\u4e2d\u56fd\u5927\u9646&#039;],50,173,8394962<\/span><\/p>\n<p>    test_para <span class=\"token operator\">&#061;<\/span> pd<span class=\"token punctuation\">.<\/span>DataFrame<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">2022<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">8.4<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">50<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">173<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    test_piaofang <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">8394962<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">10000<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u771f\u5b9e\u7968\u623f%s\u4e07&#034;<\/span> <span class=\"token operator\">%<\/span> test_piaofang<span class=\"token punctuation\">)<\/span><br \/>\n    pred_list <span class=\"token operator\">&#061;<\/span> forcast_piaofang<span class=\"token punctuation\">(<\/span>test_para<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u52a0\u8f7d\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u6a21\u578b<\/span><br \/>\n    stacking_model <span class=\"token operator\">&#061;<\/span> joblib<span class=\"token punctuation\">.<\/span>load<span class=\"token punctuation\">(<\/span>model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;stacking_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    piaofang <span class=\"token operator\">&#061;<\/span> stacking_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>pred_list<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><br \/>\n    piaofang <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">round<\/span><span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>expm1<span class=\"token punctuation\">(<\/span>piaofang<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;Stacking\u9884\u6d4b\u7968\u623f%s\u4e07&#034;<\/span> <span class=\"token operator\">%<\/span> piaofang<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> piaofang<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">forcast<\/span><span class=\"token punctuation\">(<\/span>para_list<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u6839\u636e\u4f20\u5165\u7684\u53c2\u6570\u5217\u8868&#xff0c;\u8fdb\u884c\u7968\u623f\u9884\u6d4b<\/span><br \/>\n    pred_list <span class=\"token operator\">&#061;<\/span> forcast_piaofang<span class=\"token punctuation\">(<\/span>para_list<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u52a0\u8f7d\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u6a21\u578b<\/span><br \/>\n    stacking_model <span class=\"token operator\">&#061;<\/span> joblib<span class=\"token punctuation\">.<\/span>load<span class=\"token punctuation\">(<\/span>model_save_path <span class=\"token operator\">&#043;<\/span> <span class=\"token string\">&#039;stacking_model.pkl&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    piaofang <span class=\"token operator\">&#061;<\/span> stacking_model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>pred_list<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><br \/>\n    piaofang <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">round<\/span><span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>expm1<span class=\"token punctuation\">(<\/span>piaofang<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;Stacking\u9884\u6d4b\u7968\u623f%s\u4e07&#034;<\/span> <span class=\"token operator\">%<\/span> piaofang<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> <span class=\"token string\">&#034;\u9884\u6d4b\u7968\u623f%s\u4e07(\u7f8e\u5143)&#034;<\/span> <span class=\"token operator\">%<\/span> piaofang<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">vis_relation<\/span><span class=\"token punctuation\">(<\/span>x1<span class=\"token punctuation\">,<\/span> y1<span class=\"token punctuation\">,<\/span> name1<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    fig <span class=\"token operator\">&#061;<\/span> plt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">9<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># plt.plot([0,400000000],[0,400000000],c&#061;&#034;green&#034;)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>scatter<span class=\"token punctuation\">(<\/span>x1<span class=\"token punctuation\">,<\/span> y1<span class=\"token punctuation\">,<\/span> c<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;green&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> marker<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;o&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>grid<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;piaofang&#034;<\/span><span class=\"token punctuation\">,<\/span> fontsize<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>ylabel<span class=\"token punctuation\">(<\/span>name1<span class=\"token punctuation\">,<\/span> fontsize<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;Link between piaofang and %s&#034;<\/span> <span class=\"token operator\">%<\/span>name1<span class=\"token punctuation\">,<\/span> fontsize<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>savefig<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;..\/dataset\/pictures\/piaofang_%s.png&#039;<\/span> <span class=\"token operator\">%<\/span>name1<span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>close<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5206\u6790\u7968\u623f\u9884\u6d4b\u4f7f\u7528\u7684\u6240\u6709\u5c5e\u6027\u4e0e\u7968\u623f\u4e4b\u95f4\u7684\u5173\u7cfb\u5e76\u7ed8\u5236\u6563\u70b9\u56fe&#xff0c;\u5206\u6790\u6240\u6709\u5c5e\u6027\u4e4b\u95f4\u7684\u76f8\u5173\u5ea6\u7ed8\u5236\u70ed\u529b\u56fe<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">ana_columns<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    year_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    month_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    rating_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    movie_type_count_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    country_count_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    actor_count_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    runtime_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">6<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    piaofang_list <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">.<\/span>iloc<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">7<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    vis_relation<span class=\"token punctuation\">(<\/span>piaofang_list<span class=\"token punctuation\">,<\/span> year_list<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;year&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    vis_relation<span class=\"token punctuation\">(<\/span>piaofang_list<span class=\"token punctuation\">,<\/span> month_list<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;month&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    vis_relation<span class=\"token punctuation\">(<\/span>piaofang_list<span class=\"token punctuation\">,<\/span> rating_list<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;rating&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    vis_relation<span class=\"token punctuation\">(<\/span>piaofang_list<span class=\"token punctuation\">,<\/span> movie_type_count_list<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;movie_type_count&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    vis_relation<span class=\"token punctuation\">(<\/span>piaofang_list<span class=\"token punctuation\">,<\/span> country_count_list<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;country_count&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    vis_relation<span class=\"token punctuation\">(<\/span>piaofang_list<span class=\"token punctuation\">,<\/span> actor_count_list<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;actor_count&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    vis_relation<span class=\"token punctuation\">(<\/span>piaofang_list<span class=\"token punctuation\">,<\/span> runtime_list<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;runtime&#039;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># \u76f8\u5173\u5173\u7cfb\u53ef\u89c6\u5316<\/span><br \/>\n    col <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;year&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;month&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;rating&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;movie_type_count&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;country_count&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;actor_count&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;runtime&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;piaofang&#039;<\/span><span class=\"token punctuation\">]<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>subplots<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">14<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    corr <span class=\"token operator\">&#061;<\/span> data<span class=\"token punctuation\">.<\/span>corr<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span>corr<span class=\"token punctuation\">)<\/span><br \/>\n    corr<span class=\"token punctuation\">.<\/span>to_csv<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;..\/dataset\/ana_result\/piaofang_info_corr.csv&#034;<\/span><span class=\"token punctuation\">,<\/span> encoding<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;utf-8&#039;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    sns<span class=\"token punctuation\">.<\/span>heatmap<span class=\"token punctuation\">(<\/span>corr<span class=\"token punctuation\">,<\/span> xticklabels<span class=\"token operator\">&#061;<\/span>col<span class=\"token punctuation\">,<\/span> yticklabels<span class=\"token operator\">&#061;<\/span>col<span class=\"token punctuation\">,<\/span> linewidths<span class=\"token operator\">&#061;<\/span><span class=\"token number\">.5<\/span><span class=\"token punctuation\">,<\/span> cmap<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;Reds&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>savefig<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;..\/dataset\/pictures\/corr.png&#039;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">if<\/span> __name__ <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token string\">&#039;__main__&#039;<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u56db\u4e2a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6784\u5efa\u7968\u623f\u9884\u6d4b\u6a21\u578b&#xff0c;\u7136\u540eStacking\u96c6\u6210\u6240\u6709\u7684\u7b97\u6cd5\u6a21\u578b&#xff0c;\u6784\u5efa\u6700\u7ec8\u7684\u7968\u623f\u9884\u6d4b\u6a21\u578b<\/span><br \/>\n    train_model<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u6a21\u578b\u6d4b\u8bd5<\/span><br \/>\n    piaofang <span class=\"token operator\">&#061;<\/span> test_model<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># \u5206\u6790\u7968\u623f\u9884\u6d4b\u4f7f\u7528\u7684\u6240\u6709\u5c5e\u6027\u4e0e\u7968\u623f\u4e4b\u95f4\u7684\u5173\u7cfb\u5e76\u7ed8\u5236\u6563\u70b9\u56fe&#xff0c;\u5206\u6790\u6240\u6709\u5c5e\u6027\u4e4b\u95f4\u7684\u76f8\u5173\u5ea6\u7ed8\u5236\u70ed\u529b\u56fe<\/span><br \/>\n    ana_columns<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>5\u3001\u6e90\u7801\u83b7\u53d6\u65b9\u5f0f<\/h3>\n<p>&#x1f345;\u7531\u4e8e\u7bc7\u5e45\u9650\u5236&#xff0c;\u83b7\u53d6\u5b8c\u6574\u6587\u7ae0\u6216\u6e90\u7801\u3001\u4ee3\u505a\u9879\u76ee\u7684&#xff0c;\u67e5\u770b\u6211\u7684\u3010\u7528\u6237\u540d\u3011\u3001\u3010\u4e13\u680f\u540d\u79f0\u3011\u3001\u3010\u9876\u90e8\u9009\u9898\u94fe\u63a5\u3011\u5c31\u53ef\u4ee5\u627e\u5230\u6211\u5566&#x1f345;<\/p>\n<p>\u611f\u5174\u8da3\u7684\u53ef\u4ee5\u5148\u6536\u85cf\u8d77\u6765&#xff0c;\u70b9\u8d5e\u3001\u5173\u6ce8\u4e0d\u8ff7\u8def&#xff0c;\u4e0b\u65b9\u67e5\u770b&#x1f447;&#x1f3fb;\u83b7\u53d6\u8054\u7cfb\u65b9\u5f0f&#x1f447;&#x1f3fb;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u535a\u4e3b\u4ecb\u7ecd&#xff1a;\u270c\u5168\u7f51\u7c89\u4e1d10W,\u524d\u4e92\u8054\u7f51\u5927\u5382\u8f6f\u4ef6\u7814\u53d1\u3001\u96c6\u7ed3\u7855\u535a\u82f1\u8c6a\u6210\u7acb\u5de5\u4f5c\u5ba4\u3002\u4e13\u6ce8\u4e8e\u8ba1\u7b97\u673a\u76f8\u5173\u4e13\u4e1a\u9879\u76ee\u5b9e\u62186\u5e74\u4e4b\u4e45&#xff0c;\u9009\u62e9\u6211\u4eec\u5c31\u662f\u9009\u62e9\u653e\u5fc3\u3001\u9009\u62e9\u5b89\u5fc3\u6bd5\u4e1a\u270c &gt; &#x1f345;\u60f3\u8981\u83b7\u53d6\u5b8c\u6574\u6587\u7ae0\u6216\u8005\u6e90\u7801&#xff0c;\u6216\u8005\u4ee3\u505a&#xff0c;\u62c9\u5230\u6587\u7ae0\u5e95\u90e8\u5373\u53ef\u4e0e\u6211\u8054\u7cfb\u4e86\u3002&#x1f345; \u70b9\u51fb\u67e5\u770b\u4f5c\u8005\u4e3b\u9875&#xff0c;\u4e86\u89e3\u66f4\u591a\u9879\u76ee&#xff01; &#x1f345;\u611f\u5174\u8da3\u7684\u53ef\u4ee5\u5148\u6536\u85cf\u8d77\u6765&#xff0c;\u70b9\u8d5e\u3001\u5173\u6ce8\u4e0d\u8ff7\u8def&#xff0c;\u5927\u5bb6\u5728\u6bd5\u8bbe\u9009\u9898&amp;#<\/p>\n","protected":false},"author":2,"featured_media":72959,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[153,81,50,323,5098,207,1908],"topic":[],"class_list":["post-72968","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-flask","tag-python","tag-50","tag-323","tag-5098","tag-207","tag-1908"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u7535\u5f71\u63a8\u8350\u4e0e\u7968\u623f\u9884\u6d4b\u7cfb\u7edf Python Flask \u722c\u866b Echarts \u96c6\u6210\u5b66\u4e60 \u63a8\u8350\u7b97\u6cd5 \u673a\u5668\u5b66\u4e60 \u6bd5\u4e1a\u8bbe\u8ba1\u6e90\u7801 \u5927\u6570\u636e - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/72968.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u7535\u5f71\u63a8\u8350\u4e0e\u7968\u623f\u9884\u6d4b\u7cfb\u7edf Python Flask \u722c\u866b Echarts \u96c6\u6210\u5b66\u4e60 \u63a8\u8350\u7b97\u6cd5 \u673a\u5668\u5b66\u4e60 \u6bd5\u4e1a\u8bbe\u8ba1\u6e90\u7801 \u5927\u6570\u636e - 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