# 优惠券15天内核销率 feature_Coupon[prefixs+'15_consume_rate'] = list(map(lambda x,y: x/y if y!=0 else 0, feature_Coupon[prefixs + '15days_consume_cnt'], feature_Coupon[prefixs + 'received_cnt'])) # 优惠券15天内被核销的最大时间间隔 t6 = data[data['label']==1].copy() t6['time_gap'] = (t6['date'] - t6['date_received']).map(lambda x:x.total_seconds()/(24*60*60)) pivot = pd.pivot_table(t6, index=keys4, values='time_gap', aggfunc=np.max) pivot = pd.DataFrame(pivot).rename(columns={'time_gap':prefixs + '15days_max_time_gap'}).reset_index() feature_Coupon = pd.merge(feature_Coupon, pivot, on=keys4, how='left')请用groupby（函数改写）

def get_label_feature_of_date(label_field): """提取标记区间日期相关特征""" # 源数据 data = label_field.copy() data['Coupon_id'] = data['Coupon_id'].map(int) # 将Coupon_id列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float data['Date_received'] = data['Date_received'].map(int) # 将Date_received列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float # 返回的特征数据集 feature = data.copy() feature['Week_received'] = feature['date_received'].map(lambda x: x.isoweekday()) # 星期几，星期一为1，星期天为7 feature['is_weekend_reveived'] = feature['Week_received'].map(lambda x: 1 if x == 6 or x == 7 else 0) # 判断领券日是否为休息日 feature['Month_received'] = feature['date_received'].map(lambda x: x.month)# 月份 feature = pd.concat([feature, pd.get_dummies(feature['Week_received'], prefix='Week_received')], axis=1) # one-hot离散星期几 feature.index = range(len(feature)) # 重置index # 返回 return feature解释

接着，通过lambda函数分别计算领券日是星期几、是否为休息日、月份等特征，并将星期几进行one-hot编码。最后，通过concat()函数将编码后的特征数据集与原始数据集进行合并，并通过reset_index()函数重置数据集的行...

SELECT sum(t.pay_amount)+sum(t.promotion_amount)+SUM(t.author_coupon_subsidy)+SUM(t.actual_zt_pay_promotion)+SUM(t.actual_zr_pay_promotion)+SUM(t.refund_amount)+SUM(t.jylp_wp_amount)+SUM(t.post_amount)+SUM(t.promotion_amount_yf) as 'b_amount',\n" + "SUM(t.platform_service_fee)+SUM(t.commission)+SUM(t.channel_fee)+SUM(t.colonel_service_fee)+SUM(t.channel_promotion_fee)+SUM(t.other_sharing_amount)+SUM(t.yfx_amount)+SUM(t.jylp_yp_amount) as 's_amount'\n" + "from dy_settlement_push_input t where t.deleted = '0'

3. author_coupon_subsidy：优惠券补贴金额 4. actual_zt_pay_promotion：直通车支付优惠金额 5. actual_zr_pay_promotion：直冲支付优惠金额 6. refund_amount：退款金额 7. jylp_wp_amount：加油礼品卡支付金额 8....

#商家被领取的特定优惠券数目 pivot = pd.pivot_table(data, index=keys13, values='cnt', aggfunc=lambda x:len(set(x))) pivot = pd.DataFrame(pivot).rename(columns={ 'cnt': 'merchant_differ_coupon'}).reset_index() feature = pd.merge(feature, pivot, on=keys13, how='left')解释代码函数

这段代码主要是对一个数据...接着，将聚合结果转化为DataFrame格式，并将聚合函数的名称改为'merchant_differ_coupon'，最后通过merge()函数将聚合结果与原始数据集合并，以获得每个商家被领取的特定优惠券的数量信息。

data = label_field.copy() data['Coupon_id'] = data['Coupon_id'].map(int) # 将Coupon_id列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float data['Date_received'] = data['Date_received'].map( int) # 将Date_received列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float # 返回的特征数据集 feature = data.copy() feature['week'] = feature['date_received'].map(lambda x: x.weekday()) # 星期几 feature['is_weekend'] = feature['week'].map(lambda x: 1 if x == 5 or x == 6 else 0) # 判断领券日是否为休息日 feature = pd.concat([feature, pd.get_dummies(feature['week'], prefix='week')], axis=1) # one-hot离散星期几 feature.index = range(len(feature)) # 重置index # 返回 return feature请改写这段代码而功能不变

feature = pd.concat([feature, pd.get_dummies(feature['week'], prefix='week')], axis=1) feature = feature.reset_index(drop=True) return feature 改写后的代码主要做了以下修改： 1. 使用 astype ...

name = 'Coupon_id' # 折扣率排序 datas[name_prifix + 'discount_rateRank'] = datas.groupby(name)['discount_rate'].rank(ascending = False) datas[name_prifix + 'discount_rateRankAscend'] = datas.groupby(name)['discount_rate'].rank(ascending = True) # 距离排序 datas[name_prifix + 'distanceRank'] = datas.groupby(name)['Distance'].rank(ascending = False) datas[name_prifix + 'distanceRankAscend'] = datas.groupby(name)['Distance'].rank(ascending = True) # 领券日期排序 datas[name_prifix + 'date_receivedRank'] =datas.groupby(name)['Date_received'].rank(ascending = False) datas[name_prifix + 'date_receivedRankAscend'] =datas.groupby(name)['Date_received'].rank(ascending = True) 请帮我用groupby函数改写

cols_to_rank = ['discount_rate', 'Distance', 'Date_received'] grouped = datas.groupby(name) for col in cols_to_rank: datas[name_prifix + col + 'Rank'] = grouped[col].rank(ascending=False) datas...

def get_label_feature(label_field): """提取标记区间特征，按照keys分类""" label_user_feature = get_label_user_feature(label_field) label_merchant_feature = get_label_merchant_feature(label_field) shared_characters = list(set(label_user_feature.columns.tolist()) & set(label_merchant_feature.columns.tolist())) dataset = pd.concat([label_user_feature, label_merchant_feature.drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_user_date_received_feature(label_field).drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_user_merchant_feature(label_field).drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_user_merchant_date_received_feature(label_field).drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_coupon_feature(label_field).drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_user_coupon_feature(label_field).drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_user_coupon_date_received(label_field).drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_discount_rate(label_field).drop(shared_characters, axis=1)], axis=1) dataset = pd.concat([dataset, get_label_user_discount_rate_feature(label_field).drop(shared_characters, axis=1)], axis=1) return dataset请解释该段代码详细

这段代码是一个函数，用于提取标记区间的特征，其中...11. 调用 get_label_user_discount_rate_feature 函数，提取用户使用优惠券的折扣率特征，并将其合并到 dataset 中。 12. 最后将 dataset 作为函数的输出返回。

def get_label_features_of_date(label_field): """提取标记区间日期相关特征""" # 源数据 data = label_field.copy() data['Coupon_id'] = data['Coupon_id'].map(int) # 将Coupon_id列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float data['Date_received'] = data['Date_received'].map(int) # 将Date_received列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float # 返回的特征数据集 features = data.copy() features['Week_received'] = features['date_received'].map(lambda x: x.isoweekday()) # 星期几，星期一为1，星期天为7 features['is_weekend_reveived'] = features['Week_received'].map(lambda x: 1 if x == 6 or x == 7 else 0) # 判断领券日是否为休息日 features['Month_received'] = features['date_received'].map(lambda x: x.month)# 月份 features = pd.concat([features, pd.get_dummies(features['Week_received'], prefix='Week_received')], axis=1) # one-hot离散星期几 features.index = range(len(features)) # 重置index # 返回 return features改写代码，功能不变

features['is_weekend_reveived'] = features['Week_received'].apply(lambda x: 1 if x in [6, 7] else 0) # 判断领券日是否为休息日 features['Month_received'] = features['Date_received'].apply(lambda x: x...

def get_dataset(history_field, middle_field, label_field): """构造数据集 Args: Returns: """ # 特征工程 week_feat = get_week_feature(label_field) # 日期特征 simple_feat = get_simple_feature(label_field) # 示例简单特征 # 构造数据集 share_characters = list( set(simple_feat.columns.tolist()) & set(week_feat.columns.tolist())) # 共有属性,包括id和一些基础特征,为每个特征块的交集 dataset = pd.concat([week_feat, simple_feat.drop(share_characters, axis=1)], axis=1) # 删除无用属性并将label置于最后一列 if 'Date' in dataset.columns.tolist(): # 表示训练集和验证集 dataset.drop(['Merchant_id', 'Discount_rate', 'Date', 'date_received', 'date'], axis=1, inplace=True) label = dataset['label'].tolist() dataset.drop(['label'], axis=1, inplace=True) dataset['label'] = label else: # 表示测试集 dataset.drop(['Merchant_id', 'Discount_rate', 'date_received'], axis=1, inplace=True) # 修正数据类型 dataset['User_id'] = dataset['User_id'].map(int) dataset['Coupon_id'] = dataset['Coupon_id'].map(int) dataset['Date_received'] = dataset['Date_received'].map(int) dataset['Distance'] = dataset['Distance'].map(int) if 'label' in dataset.columns.tolist(): dataset['label'] = dataset['label'].map(int) # 去重 dataset.drop_duplicates(keep='first', inplace=True) dataset.index = range(len(dataset)) # 返回 return dataset为什么

接着，对数据类型进行修正，包括将用户ID、优惠券ID、领取日期和距离转换为整数类型。如果数据集中包含标签列，则将标签列也转换为整数类型。最后，通过“drop_duplicates”函数去重，并重新设置数据集的索引。...

def get_label_features_of_date(label_field): # 源数据 data = label_field.copy() data['Coupon_id'] = data['Coupon_id'].astype(int) # 将Coupon_id列中float类型的元素转换为int类型 data['Date_received'] = data['Date_received'].astype(int) # 将Date_received列中float类型的元素转换为int类型 # 返回的特征数据集 features = data.copy() features['Week_received'] = features['Date_received'].apply(lambda x: x.isoweekday()) # 星期几，星期一为1，星期天为7 features['is_weekend_reveived'] = features['Week_received'].apply(lambda x: 1 if x in [6, 7] else 0) # 判断领券日是否为休息日 features['Month_received'] = features['Date_received'].apply(lambda x: x.month) # 月份 features = pd.concat([features, pd.get_dummies(features['Week_received'], prefix='Week_received')], axis=1) # one-hot离散星期几 features.reset_index(drop=True, inplace=True) # 重置index # 返回 return features出现报错 AttributeError: 'int' object has no attribute 'isoweekday' 如何解决

这个报错是因为代码中的变量 x 的类型是 int，而 int 类型没有 isoweekday() 方法。通常出现这种错误是因为数据类型转换出现问题。建议检查数据集中 Date_received 列中是否存在 float 类型的元素，如果有需要将其...

datasets_yc = pd.merge(datasets_yc,coupon_rate,how='left',on='Coupon_id') datasets_yc = pd.merge(datasets_yc,user_mer_num,how='left',on=['User_id','Merchant_id']) datasets_yc = pd.merge(datasets_yc,user_mer_coupon,how='left',on=['User_id','Merchant_id']) datasets_yc = pd.merge(datasets_yc,user_mer,how='left',on=['User_id','Merchant_id']) datasets_yc['user_mer_cop_rate'] = datasets_yc['user_mer_num'] / datasets_yc['user_mer_coupon']

这段代码的作用是通过 merge 函数将多个 DataFrame 进行合并，并计算出 user_mer_cop_rate 列的值。这里可以对代码进行优化，使用 merge 函数的链式调用，可以简化代码并提高代码的可读性： python ...

select <include refid="Base_Column_List" /> from t_mk_coupon_history where coupon_history_id = #{couponHistoryId,jdbcType=VARCHAR}这个语法解释一下

这段代码是一个基本的 SQL 查询语句，用于从表 t_mk_coupon_history 中查询数据。 <include> 元素用于引用其他的 SQL 片段，其中 refid 属性指定了要引用的 SQL 片段的标识符。在这个例子中，被引用的 SQL ...

#如果用户领取特定优惠券2次以上，那么提取出第一次和最后一次领取的时间 t2=dataset3[['user_id','coupon_id','date_received']] t2.date_received=t2.date_received.astype('str') t2=t2.groupby(['user_id','coupon_id'])['date_received'].agg(lambda x:':'.join(x)).reset_index() t2['receive_number']=t2.date_received.apply(lambda s:len(s.split(':'))) t2=t2[t2.receive_number>1] t2['max_date_received']=t2.date_received.apply(lambda s:max([int(d) for d in s.split(':')])) t2['min_date_received']=t2.date_received.apply(lambda s:min([int(d) for d in s.split(':')])) t2=t2[['user_id','coupon_id','max_date_received','min_date_received']] 详细解释这段代码

这段代码是对一个数据集进行处理，其目的是从数据集中提取出用户领取特定优惠券的第一次和最后一次领取时间。具体解释如下： 1. 首先，代码创建一个名为t2的新数据集，并将原始数据集中的'user_id'、'coupon_id'和'...

def model_xgb(train, test): """xgb模型 Args: Returns: """ # xgb参数 params = {'booster': 'gbtree', 'objective': 'binary:logistic', 'eval_metric': 'auc', 'silent': 1, 'eta': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'lambda': 1, 'colsample_bylevel': 0.7, 'colsample_bytree': 0.7, 'subsample': 0.9, 'scale_pos_weight': 1} # 数据集 dtrain = xgb.DMatrix(train.drop(['User_id', 'Coupon_id', 'Date_received', 'label'], axis=1), label=train['label']) dtest = xgb.DMatrix(test.drop(['User_id', 'Coupon_id', 'Date_received'], axis=1)) # 训练 watchlist = [(dtrain, 'train')] model = xgb.train(params, dtrain, num_boost_round=500, evals=watchlist) # 预测 predict = model.predict(dtest) # 处理结果 predict = pd.DataFrame(predict, columns=['prob']) result = pd.concat([test[['User_id', 'Coupon_id', 'Date_received']], predict], axis=1) # 特征重要性 feat_importance = pd.DataFrame(columns=['feature_name', 'importance']) feat_importance['feature_name'] = model.get_score().keys() feat_importance['importance'] = model.get_score().values() feat_importance.sort_values(['importance'], ascending=False, inplace=True) # 返回 return result, feat_importance解释一下

1. 定义 XGBoost 模型的参数：包括 booster（使用的树模型）、objective（目标函数）、eval_metric（评价指标）、eta（学习率）、max_depth（树的最大深度）等。 2. 将训练数据集和测试数据集转换为 ...

SELECT c.stock_id, a.stock_name, a.coupon_amount, a.transaction_minimum, b.company_name, a.start_time, a.end_time, (SELECT COUNT(1) FROM user_coupon WHERE activity_id = a.id) AS total_grant, (SELECT COUNT(1) FROM user_coupon WHERE activity_id = a.id AND (status = "20" OR status = "30")) AS receive, (SELECT COUNT(1) FROM user_coupon WHERE activity_id = a.id AND status = "30") AS writeOff FROM wx_coupon a LEFT JOIN baishiof_company b ON a.company_id = b.company_id LEFT JOIN user_coupon c ON a.id = c.activity_id GROUP BY c.stock_id, a.stock_name, a.coupon_amount, a.transaction_minimum, b.company_name, a.start_time, a.end_time;以上代码存在这个问题Expression #8 of SELECT list is not in GROUP BY clause and contains nonaggregated column 'baishiof.a.id' which is not functionally dependent on columns in GROUP BY clause; this is incompatible with sql_mode=only_full_group_by修复他

这样可以解决 "Expression #8 of SELECT list is not in GROUP BY clause and contains nonaggregated column 'baishiof.a.id'" 这个错误。以下是修复后的代码示例： sql SELECT c.stock_id, a.stock_name, a...

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#商家被领取的特定优惠券数目 pivot = pd.pivot_table(data, index=keys13, values='cnt', aggfunc=lambda x:len(set(x))) pivot = pd.DataFrame(pivot).rename(columns={ 'cnt': 'merchant_differ_coupon'}).reset_index() feature = pd.merge(feature, pivot, on=keys13, how='left')解释代码函数

select <include refid="Base_Column_List" /> from t_mk_coupon_history where coupon_history_id = #{couponHistoryId,jdbcType=VARCHAR}这个语法解释一下

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