天池优惠卷预测--XGBoost训练模型

r囧r小猫 2022-05-15 05:44 271阅读 0赞

### 训练集 ###

经过上一个步骤之后，我们可以得到训练的特征数据集，这里一共有两个数据集，按日期分的，接下来利用训练集里得到模型。

### XGBoost模型 ###

这里讲解利用XGBoost模型来训练模型，首先需要在python中安装XGBoost，安装步骤如下：

*  1、anaconda search -t conda xgboost  
    
 *  2、conda install -c anaconda py-xgboost  
    第一个命令是搜索xgboost库，第二个命令是安装搜索到的py-xgboost模型。安装好之后，开始训练模型。
 *  
 *  1、引包
 *      from sklearn.metrics import confusion_matrix, roc_auc_score
        import numpy as np
        import xgboost
        import pandas as pd
        import matplotlib.pyplot as plt
        import time
        import os
        import sys
        import json
        import operator
 *  2、文件路径
 *      save_stdout = sys.stdout
        
        fileDir='D:/workspace/gitWorkSpace/O2O-Coupon-Usage-Forecast-master/O2O-Coupon-Usage-Forecast-master/code/wepon/data1/'
        
        # 特征
        train_path=fileDir+'data/dataset1.csv'
        validate_path=fileDir+'data/dataset2.csv'
        predict_path=fileDir+'data/dataset3.csv'
        
        model_path=fileDir+'model/trainModel'
        model_fmap_file = 'model.fmap'
        # model path
        model_file ='.model'
        model_dump_file = 'model_dump.txt'
        model_feature_importance_file = 'feature_importance.png'
        model_feature_importance_csv = 'feature_importance.csv'
        model_train_log = 'train.log'
        model_params = '/param.json'
        
        val_diff_file ='val_diff.csv'
        # submission path
        submission_path = fileDir+'submission'
        submission_hist_file = 'hist.png'
        submission_file = 'submission.csv'
        
        # raw field name
        user_label = 'User_id'
        merchant_label = 'Merchant_id'
        coupon_label = 'Coupon_id'
        action_label = 'Action'
        discount_label = 'Discount_rate'
        distance_label = 'Distance'
        date_received_label = 'Date_received'
        date_consumed_label = 'Date'
        probability_consumed_label = 'Probability'
        
        column=["user_id","discount_rate","distance","day_of_month","days_distance","discount_man","discount_jian","is_man_jian","total_sales","sales_use_coupon","total_coupon","merchant_min_distance","merchant_max_distance","merchant_mean_distance","merchant_median_distance","merchant_coupon_transfer_rate","coupon_rate","count_merchant","user_min_distance","user_max_distance","user_mean_distance","user_median_distance","buy_use_coupon","buy_total","coupon_received","avg_user_date_datereceived_gap","min_user_date_datereceived_gap","max_user_date_datereceived_gap","buy_use_coupon_rate","user_coupon_transfer_rate","user_merchant_buy_total","user_merchant_received","user_merchant_buy_use_coupon","user_merchant_any","user_merchant_buy_common","user_merchant_coupon_transfer_rate","user_merchant_coupon_buy_rate","user_merchant_rate","user_merchant_common_buy_rate","this_month_user_receive_same_coupon_count","this_month_user_receive_all_coupon_count","this_month_user_receive_same_coupon_lastone","this_month_user_receive_same_coupon_firstone","this_day_user_receive_all_coupon_count","this_day_user_receive_same_coupon_count","day_gap_before","day_gap_after","is_weekend","weekday1","weekday2","weekday3","weekday4","weekday5","weekday6","weekday7","label"]
    
    3、函数方法
    
        def calc_auc(df):
            coupon = df[coupon_label].iloc[0]
            y_true = df['label'].values
            if len(np.unique(y_true)) != 2:
                auc = np.nan
            else:
                y_pred = df[probability_consumed_label].values
                auc = roc_auc_score(np.array(y_true), np.array(y_pred))
            return pd.DataFrame({coupon_label: [coupon], 'auc': [auc]})
        
        
        def check_average_auc(df):
            grouped = df.groupby(coupon_label, as_index=False).apply(lambda x: calc_auc(x))
            return grouped['auc'].mean(skipna=True)
        
        
        def create_feature_map(features, fmap):
            outfile = open(fmap, 'w')
            for i, feat in enumerate(features):
                outfile.write('{0}\t{1}\tq\n'.format(i, feat))
            outfile.close()
        
        
        def train(param, num_round=1000, early_stopping_rounds=20):
            exec_time = time.strftime("%Y%m%d%I%p%M", time.localtime())
        
            os.mkdir('{0}_{1}'.format(model_path, exec_time))
            os.mkdir('{0}_{1}'.format(submission_path, exec_time))
        
            train_params = param.copy()
            train_params['num_boost_round'] = num_round
            train_params['early_stopping_rounds'] = early_stopping_rounds
            # 使用json.dumps()方法来将一个Python数据类型列表编码成json格式的字符串。
            # 这里暂时报错，先注释掉
            #json.dump(train_params, open('{0}_{1}{2}'.format(model_path, exec_time, model_params), 'wb+'))
        
            print('get training data')
        
            train_features = pd.read_csv(train_path).astype(float)
            train_features.columns = column
            # 获取所有数据，1、得到标签 
            train_labels = train_features.label
            # 2、去掉标签
            train_features = train_features.drop(['label'],axis=1)
        
            # 交叉验证集
            validate_features = pd.read_csv(validate_path).astype(float)
            validate_features.columns = column
        
            validate_labels = validate_features.label
            validate_features = validate_features.drop(['label'],axis=1)
        
            # 测试集
            predict_features = pd.read_csv(predict_path).astype(float)
        
            # 生成一个文件
            create_feature_map(train_features.columns.tolist(), '{0}_{1}{2}'.format(model_path, exec_time, model_fmap_file))
        
            # XGBoost可以加载libsvm格式的文本数据，加载的数据格式可以为Numpy的二维数组和XGBoost的二进制的缓存文件。加载的数据存储在对象DMatrix中。
            train_matrix = xgboost.DMatrix(train_features.values, label=train_labels.values, feature_names=train_features.columns)
            val_matrix = xgboost.DMatrix(validate_features.values, label=validate_labels.values, feature_names=validate_features.columns)
            predict_matrix = xgboost.DMatrix(predict_features.values, feature_names=predict_features.columns)
        
            watchlist = [(train_matrix, 'train'), (val_matrix, 'eval')]
        
            print ('model training')
        # with open('{0}_{1}{2}'.format(model_path, exec_time, model_train_log), 'wb+') as outf:
        # sys.stdout = outf
        # model = xgboost.train(param, train_matrix, num_boost_round=num_round, evals=watchlist, early_stopping_rounds=early_stopping_rounds)
        
            model = xgboost.train(param, train_matrix, num_boost_round=num_round, evals=watchlist, early_stopping_rounds=early_stopping_rounds)
        
            sys.stdout = save_stdout
            print('model.best_score: {0}, model.best_iteration: {1}, model.best_ntree_limit: {2}'.format(model.best_score, model.best_iteration, model.best_ntree_limit))
        
            print ('output offline model data')
            model.save_model('{0}_{1}{2}'.format(model_path, exec_time, model_file))
            model.dump_model('{0}_{1}{2}'.format(model_path, exec_time, model_dump_file))
        
            importance = model.get_fscore(fmap='{0}_{1}{2}'.format(model_path, exec_time, model_fmap_file))
            importance = sorted(importance.items(), key=operator.itemgetter(1))
            df = pd.DataFrame(importance, columns=['feature', 'fscore'])
            df['fscore'] = df['fscore'] / df['fscore'].sum()
            df.to_csv('{0}_{1}{2}'.format(model_path, exec_time, model_feature_importance_csv), index=False)
        
            xgboost.plot_importance(model)
            plt.gcf().set_size_inches(20, 16)
            plt.gcf().set_tight_layout(True)
            plt.gcf().savefig('{0}_{1}{2}'.format(model_path, exec_time, model_feature_importance_file))
            plt.close()
        
            train_pred_labels = model.predict(train_matrix, ntree_limit=model.best_ntree_limit)
            val_pred_labels = model.predict(val_matrix, ntree_limit=model.best_ntree_limit)
        
            train_pred_frame = pd.Series(train_pred_labels, index=train_features.index)
            train_pred_frame.name = probability_consumed_label
            val_pred_frame = pd.Series(val_pred_labels, index=validate_features.index)
            val_pred_frame.name = probability_consumed_label
        
            ## 
            data_split_path= 'D:/workspace/gitWorkSpace/O2O-Coupon-Usage-Forecast-master/O2O-Coupon-Usage-Forecast-master/code/charles/data_split'
            train_true_frame = pd.read_csv( train_path )['label']
            val_true_frame = pd.read_csv( validate_path )['label']
            train_coupons = pd.read_csv( data_split_path+"/train_data/dataset.csv" )   # 这个地方数据要分片，切分数据 
            val_coupons = pd.read_csv( data_split_path+"/validate_data/dataset.csv" )   # 切分的原始数据，根据日期切分的数据 
        
            train_check_matrix = train_coupons[[coupon_label]].join(train_true_frame).join(train_pred_frame)
            val_check_matrix = val_coupons[[coupon_label]].join(val_true_frame).join(val_pred_frame)
            print('Average auc of train matrix: ', check_average_auc(train_check_matrix))
            print('Average auc of validate matrix', check_average_auc(val_check_matrix))
        
            val_coupons = val_coupons.join(val_pred_frame).join(val_pred_frame.map(lambda x: 0. if x < 0.5 else 1.).rename('map')).join(val_true_frame)
            val_coupons.to_csv('{0}_{1}{2}'.format(model_path, exec_time, val_diff_file), index=False)
            print(confusion_matrix(val_coupons['label'], val_coupons['map']))
        
            # 预测模型
            labels = model.predict(predict_matrix, ntree_limit=model.best_ntree_limit)
            frame = pd.Series(labels, index=predict_features.index)
            frame.name = probability_consumed_label
        
            plt.figure()
            frame.hist(figsize=(10, 8))
            plt.title('results histogram')
            plt.xlabel('predict probability')
            plt.gcf().savefig('{0}_{1}{2}'.format(submission_path, exec_time, submission_hist_file))
            plt.close()
        
            submission = pd.read_csv(data_split_path+"/predict_data/dataset.csv")
            submission = submission[[user_label, coupon_label, date_received_label]].join(frame)  # 把核销概率拼接在一起
            submission.to_csv('{0}_{1}{2}'.format(submission_path, exec_time, submission_file), index=False)
 *  4、主函数
 *      if __name__ == '__main__':
            # 调参  参数说明
            # param = dict()
            # param['objective'] = 'binary:logistic'
            # param['eval_metric'] = 'auc'
            # param['silent'] = 1
            # param['scale_pos_weight'] = 10
            # all_etas = [0.01, 0.05, 0.1, 0.15, 0.2]
            # all_subsamples = [0.6, 0.8, 1.0]
            # all_colsample_bytree = [0.6, 0.8, 1.0]
            # all_depth = [6, 7, 8, 9]
            # all_child_weights = [1, 10, 20, 50]
            # all_gamma = [0, 5, 20, 50]
            # for e, s, cb, d, cw, g in list(itertools.product(all_etas, all_subsamples, all_colsample_bytree, all_depth, all_child_weights, all_gamma)):
            # param['eta'] = e
            # param['subsample'] = s
            # param['colsample_bytree'] = cb
            # param['max_depth'] = d
            # param['min_child_weight'] = cw
            # param['gamma'] = g
            # train(param)
        
            init_param = {
                'max_depth': 8,
                'eta': 0.1,
                'silent': 1,
                'seed': 13,
                'objective': 'binary:logistic',
                'eval_metric': 'auc',
                'scale_pos_weight': 2,
                'subsample': 0.8,
                'colsample_bytree': 0.7,
                'min_child_weight': 100,
                'max_delta_step': 20
            }
            train(init_param, num_round=1000, early_stopping_rounds=50)
            print("结束")
    
    用两份数据集训练模型，并分别用来预测另一个训练集，计算其准确率，训练完模型保存了起来，最后我们会用训练得到的模型来预测测试集数据。