代码讨论会议-关于noahlab和

NoahLab一 DataScalerself.scaler=StandardScaler() #Standardizefeaturesbyremovingthemeanandscalingtounitvariance定义pca或pls对数据进行降维处理（pls需同时传入样本和transform和二 CommonModelSingleton利用datakeeper一系列数据参数，并获得样本lable和common模型得到样本数据，参数，集；用pls来normalizestatedata，normalizeparameterdata；用现有参数，训练bagging和QR模型作为common模型，并给出训练数据集的值；用定义的评价函数给出score_r2值。得到样本数据，参数，集；得到参数取值范围到use_action_set中；if(config.epsilon_greedy==True)，用choice随机下发参数；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实值误差95%的样本。用训练好的common模型和self模型给出训练样本的值，并用三种评价函数给出score #addfeature-numbertocolumnsforpara_indinrange(action_dim):#arrangeallthepossibleparameterstoeachcellandcalculatethelossoneachconditionthenselecttheminimallossbyusingparameter（455起）#遍历参数可能取值，把每个可能取值的参数和当前测试数据一一打包X_test_df=pd.concat(X_test_df_lst) 用训练好的模型给出测试数据集的coef_estimator=Pipeline([('poly',PolynomialFeatures(degree=2)), state_idx_df=X_test_df.iloc[list(idxs)] 利用coef对测试数据集进行（去除偏差过大的样本）selectminimallossparameterforparam_valin 记录minimallossparameter，更新最小损调用get_action_by_model True，正态分布探索（没有用）461：测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=#将处理后 sqlformula逐个查找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=根据读出con_list；把所有colname存在total_foumulacon_list-- forsingle_formulaincolumn_names,'alldata')tem_cal为当前所选状态的temp_cal-- alldata[dst_name]=把temp_cal的值给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]训练线性模型，并给出值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