NoahLab一 DataScalerself.scaler=StandardScaler() #Standardizefeaturesbyremovingthemeanandscalingtounitvariance定義pca或pls對數(shù)據(jù)進(jìn)行降維處理（pls需同時傳入樣本和transform和二 CommonModelSingleton利用datakeeper一系列數(shù)據(jù)參數(shù)，并獲得樣本lable和common模型得到樣本數(shù)據(jù)，參數(shù)，集；用pls來normalizestatedata，normalizeparameterdata；用現(xiàn)有參數(shù)，訓(xùn)練bagging和QR模型作為common模型，并給出訓(xùn)練數(shù)據(jù)集的值；用定義的評價函數(shù)給出score_r2值。得到樣本數(shù)據(jù)，參數(shù)，集；得到參數(shù)取值范圍到use_action_set中；if(config.epsilon_greedy==True)，用choice隨機(jī)下發(fā)參數(shù)；chosen_action= min(use_action_set[jjmin(use_action_set[jj])),max(use_action_set[jjforjjinrange(action_dim)] else:chosen_action=[cell_train_data[parameter].values[0,jj]forjjin #usedefaultparametersin1stday實值誤差95%的樣本。用訓(xùn)練好的common模型和self模型給出訓(xùn)練樣本的值，并用三種評價函數(shù)給出score #addfeature-numbertocolumnsforpara_indinrange(action_dim):#arrangeallthepossibleparameterstoeachcellandcalculatethelossoneachconditionthenselecttheminimallossbyusingparameter（455起）#遍歷參數(shù)可能取值，把每個可能取值的參數(shù)和當(dāng)前測試數(shù)據(jù)一一打包X_test_df=pd.concat(X_test_df_lst) 用訓(xùn)練好的模型給出測試數(shù)據(jù)集的coef_estimator=Pipeline([('poly',PolynomialFeatures(degree=2)), state_idx_df=X_test_df.iloc[list(idxs)] 利用coef對測試數(shù)據(jù)集進(jìn)行（去除偏差過大的樣本）selectminimallossparameterforparam_valin 記錄minimallossparameter，更新最小損調(diào)用get_action_by_model True，正態(tài)分布探索（沒有用）461：測son_para_names=datakeeper.getSonParaDefsColVal(son_name, son_para_names=xs_tag=datakeeper.feature_dict[son_name] #tagfeatureson_eval_y_expr=son_eval_y_expr_ex=#將處理后 sqlformula逐個查找counterlst的字段，如果找到，在之前加上dfname字符串xpr:str,cell_eval_user_tag:str,son_para_names:list,son_eval_condition_expr--> con_list,total_formula=根據(jù)讀出con_list；把所有colname存在total_foumulacon_list-- forsingle_formulaincolumn_names,'alldata')tem_cal為當(dāng)前所選狀態(tài)的temp_cal-- alldata[dst_name]=把temp_cal的值給alldata[dst_name]-->model=PolynomialFeatures(degree=3)regressor_model.fit(x_train_poly,alldata[dst_name])alldata[base_name]=regressor_model.predict(x_train_poly)用x_train_poly,alldata[dst_name]訓(xùn)練線性模型，并給出值alldata[single_formula[2]]=(alldata[dst_name]-total_formula=dataFrameSqlGen(total_formula,[x[2]forxincon_list],'alldata')alldata['SON_COND_RES']=eval(total_formula)st