# 对字符类型的缺失值进行填充 cols = ["customer_province" , "customer_city"] for col in cols: full[col].fillna("None",inplace=True)# 对数值类型的缺失值进行填充 cols1=["is_customer_rate", "customer_gender", "is_member_actived"] for col in cols1: full[col].fillna(0, inplace=True)# 对这些列用众数进行填充 cols2 = ["customer_id","member_status"] for col in cols2: full[col].fillna(full[col].mode()[0], inplace=True)

# 针对customer_province处理 customer_province_cold = ['香港特别行政区','台湾','柔佛'] customer_province_hot1 = ['广东省','浙江省','江苏省','上海','北京','None'] customer_province_hot2 = ['四川省','湖北省','山东省','河南省','福建省','安徽省','湖南省'] customer_province_hot3 = ['辽宁省','江西省','重庆','河北省','陕西省','云南省'] customer_province_hot4 = ['广西壮族自治区','天津','黑龙江省','贵州省','吉林省', '山西省','内蒙古自治区','新疆维吾尔自治区','甘肃省', '海南省','西藏自治区','宁夏回族自治区','青海省']for province_col_cold in customer_province_cold: full.loc[full.customer_province == province_col_cold, 'customer_province'] = 0for province_col_hot1 in customer_province_hot1: full.loc[full.customer_province == province_col_hot1, 'customer_province'] = 1for province_col_hot2 in customer_province_hot2: full.loc[full.customer_province == province_col_hot2, 'customer_province'] = 2for province_col_hot3 in customer_province_hot3: full.loc[full.customer_province == province_col_hot3, 'customer_province'] = 3for province_col_hot4 in customer_province_hot4: full.loc[full.customer_province == province_col_hot4, 'customer_province'] = 4

# 针对customer_city处理 full_customer_city = full['customer_city'].drop_duplicates().dropna().values.tolist() customer_city_hot1 = ['上海市','None','北京市','广州市','杭州市','成都市','武汉市','深圳市'] customer_city_hot2 = ['南京市','重庆市','苏州市','天津市','长沙市','郑州市','西安市'] customer_city_hot3 = ['合肥市','宁波市','福州市','昆明市','温州市','东莞市','青岛市', '沈阳市','厦门市','佛山市','南昌市','无锡市','济南市','哈尔滨市'] customer_city_cold = list(set(full_customer_city) - set(customer_city_hot1) - set(customer_city_hot2) - set(customer_city_hot3))for city_col_hot1 in customer_city_hot1: full.loc[full.customer_city == city_col_hot1, 'customer_city'] = 1for city_col_hot2 in customer_city_hot2: full.loc[full.customer_city == city_col_hot2, 'customer_city'] = 2for city_col_hot3 in customer_city_hot3: full.loc[full.customer_city == city_col_hot3, 'customer_city'] = 3for city_col_cold in customer_city_cold: full.loc[full.customer_city == city_col_cold, 'customer_city'] = 0

from sklearn.preprocessing import LabelEncoder#标签编码 lab = LabelEncoder()#full["customer_province"] = lab.fit_transform(full.customer_province).astype('int64') #full["customer_city"] = lab.fit_transform(full.customer_city).astype('int64')# 浮点转为整型 full["customer_city"] = full["customer_city"].astype('int64') full["customer_gender"] = full["customer_gender"].astype('int64') full["member_status"] = full["member_status"].astype('int64') full["is_member_actived"] = full["is_member_actived"].astype('int64') full["is_customer_rate"] = full["is_customer_rate"].astype('int64') full["customer_id"] = full["customer_id"].astype('int64')# 利用ID full.loc[full.member_id !=0, 'member_id'] = 1

# 查看order_pay_time的范围 print('full date gap',full.order_pay_time.min(),'->',full.order_pay_time.max())

full['order_pay_time'] = pd.to_datetime(full['order_pay_time'])# 转换日期格式 例：2013-01-01 14:25:30 full['order_pay_date'] = full['order_pay_time'].dt.date# 只取日期时间date部分 例：2013-01-01 # 取验证集开始的时间，即2013-07-04 validata_date_begin = full['order_pay_date'].max() - datetime.timedelta(days=180) # timedelta模块 时间增减用法# full前180天 order_pay_date 的数据 train_history = full[(full['order_pay_date'].astype(str)<='2013-07-03')]# astype() 实现变量类型转换 # full360天 order_pay_date 的数据 online_history = full[(full['order_pay_date'].astype(str)<='2013-12-31')] # train_label 相对于 train_history 的未来180天的数据 train_label = full[full['order_pay_date'].astype(str)>='2013-07-04']

# 使数据有折扣和值为19.99，9.90，19.90，0.01—>order_total_discount = 1 train_history.loc[train_history.order_total_discount>0, 'order_total_discount'] = 1 online_history.loc[online_history.order_total_discount>0, 'order_total_discount'] = 1order_amount_nums = [19.99,9.90,19.90,0.01] for order_amount_num in order_amount_nums: train_history.loc[train_history.order_amount==order_amount_num, 'order_total_discount'] = 1 online_history.loc[online_history.order_amount==order_amount_num, 'order_total_discount'] = 1

# make label train_history['label'] = 0 train_history.loc[train_history['customer_id'].isin(list(train_label['customer_id'].unique())),'label'] = 1# isin()接受一个列表，判断该列中元素是否在列表中。 train_history['label'].value_counts()

# 重新制作训练集和测试集的特征 def make_new_feature1(feature,isSubmit): feature['count'] = 1 # 统计单个用户出现了多少次 customer_id_count = feature.groupby(['customer_id'],as_index=False)['count'].agg({'count':'count'}) # 统计单个用户第一次和最后一次购买订单时间 last_time = feature.groupby(['customer_id'],as_index=False)['order_pay_date'].agg({'order_pay_date_first':'min', 'order_pay_date_last':'max'}) # 合并以上数据 print('start to merge!')data = https://www.it610.com/article/pd.merge(customer_id_count,last_time,on=['customer_id'],how='left',copy=False) # 记录购买间隔时间 data['pay_interval_time'] = pd.to_datetime(data['order_pay_date_last']) - pd.to_datetime(data['order_pay_date_first']) data['pay_interval_time'] = data['pay_interval_time'].dt.days + 1 # 购买距离双十一时间 data['pay_11interval_time'] = pd.to_datetime('2013-11-11') - pd.to_datetime(data['order_pay_date_last']) data['pay_11interval_time'] = data['pay_11interval_time'].dt.days + 1 del data['order_pay_date_first'] if isSubmit==False: data['order_pay_date_last'] = pd.to_datetime('2013-07-03') - pd.to_datetime(data['order_pay_date_last']) data['order_pay_date_last'] = data['order_pay_date_last'].dt.days + 1 else: data['order_pay_date_last'] = pd.to_datetime('2013-12-31') - pd.to_datetime(data['order_pay_date_last']) data['order_pay_date_last'] = data['order_pay_date_last'].dt.days + 1print(data.shape) return data

# 生成训练数据和提交数据 train1 = make_new_feature1(train_history,False) test1 = make_new_feature1(online_history,True)

# 超过双十一都改为距离下一年双十一的时间 pay_11interval_time_neg = test1.loc[test1.pay_11interval_time <0, 'pay_11interval_time'] test1.loc[test1.pay_11interval_time <0, 'pay_11interval_time'] = 365 + pay_11interval_time_neg

# 添加新的特征 def make_new_feature2(feature, train1): # 性别 customer_gender = feature.groupby(['customer_id'],as_index=False)['customer_gender'].agg({'customer_gender':'max'}) # 用户所在省市 customer_province = feature.groupby(['customer_id'],as_index=False)["customer_province"].agg({'customer_province':'max'}) customer_city = feature.groupby(['customer_id'],as_index=False)["customer_city"].agg({'customer_city':'median'})# 支持会员折扣 goods_has_discount = feature.groupby(['customer_id'],as_index=False)["goods_has_discount"].agg({'goods_has_discount_count':'count'}) #'goods_has_discount_sum':'sum'}) #'goods_has_discount_min':'min'})# 用户评价的次数以及用户评价率 def evaluate_rate_computer(group): return (group.sum()) / (group.count()) evaluate_count = feature.groupby(['customer_id'],as_index=False)["is_customer_rate"].agg({'evaluate_count':'sum', 'evaluate_rate':evaluate_rate_computer})# 用户是否是会员 is_member = feature.groupby(['customer_id'],as_index=False)["member_status"].agg({'is_member':'mean'}) #is_member = feature.groupby(['customer_id'],as_index=False)["member_id"].agg({'is_member':'max'})# 合并以上数据 print('start to merge!')data = https://www.it610.com/article/pd.merge(train1,customer_gender,on=['customer_id'],how='left',copy=False) data = https://www.it610.com/article/pd.merge(data,customer_province,on=['customer_id'],how='left',copy=False) data = https://www.it610.com/article/pd.merge(data,customer_city,on=['customer_id'],how='left',copy=False) data = https://www.it610.com/article/pd.merge(data,goods_has_discount,on=['customer_id'],how='left',copy=False) data = https://www.it610.com/article/pd.merge(data,evaluate_count,on=['customer_id'],how='left',copy=False) data = https://www.it610.com/article/pd.merge(data,is_member,on=['customer_id'],how='left',copy=False)print(data.shape) return data

# 生成训练数据和提交数据 train2 = make_new_feature2(train_history, train1) test2 = make_new_feature2(online_history, test1)

def make_new_feature3(feature, train2): # 统计这个用户购买商品的价格信息 good_price = feature.groupby(['customer_id'],as_index=False)['goods_price'].agg({'goods_price_max':'max', 'goods_price_min':'min', 'goods_price_mean':'mean'}) ## 订单应付总金额 #order_amount = feature.groupby(['customer_id'],as_index=False)['order_amount'].agg({'order_amount_max':'max', #'order_amount_min':'min', #'order_amount_sum':'sum'}) # 订单优惠金额 order_total_discount = feature.groupby(['customer_id'],as_index=False)['order_total_discount'].agg({'order_total_discount_sum':'sum'}) ##'order_total_discount_min':'min', ##'order_total_discount_max':'max'})# 合并以上数据 print('start to merge!')data = https://www.it610.com/article/pd.merge(train2,good_price,on=['customer_id'],how='left',copy=False) #data = https://www.it610.com/article/pd.merge(data,order_amount,on=['customer_id'],how='left',copy=False) data = https://www.it610.com/article/pd.merge(data,order_total_discount,on=['customer_id'],how='left',copy=False)print(data.shape) return data

# 生成训练数据和提交数据 train3 = make_new_feature3(train_history, train2) test3 = make_new_feature3(online_history, test2)

def make_label(labels,train3): label = labels.groupby(['customer_id'],as_index=False)['label'].agg({'label':'max'})data = https://www.it610.com/article/pd.merge(train3,label,on=['customer_id'],how='left',copy=False) print(data.shape) return data

train = make_label(train_history,train3) test = test3.copy() print(test.shape)

train["customer_city"] = train["customer_city"].astype('int64') test["customer_city"] = test["customer_city"].astype('int64') train["order_total_discount_sum"] = train["order_total_discount_sum"].astype('int64') test["order_total_discount_sum"] = test["order_total_discount_sum"].astype('int64')

# 构建机器学习所需的label和data train_copy = train.copy() y = train_copy.pop('label') feature = [x for x in train_copy.columns if x not in ['customer_id']] X = train_copy[feature]# 训练与测试数据标准化处理 from sklearn.preprocessing import StandardScaler ss_X = StandardScaler() X_scaled = ss_X.fit_transform(X)# 划分训练集和验证集 X_train, X_valid, y_train, y_valid = train_test_split(X_scaled, y, test_size=0.5, random_state=33)

# 参数 param = { 'num_leaves':40, 'objective':'binary', 'max_depth':5, 'learning_rate':0.1, 'metric':'binary_logloss'}test_df = test[['customer_id']] X_test = test[feature] # train_data = https://www.it610.com/article/lgb.Dataset(X_train, label=y_train) valid_data = lgb.Dataset(X_valid, label=y_valid) lgbm = lgb.train(param,train_data,valid_sets=[train_data,valid_data], num_boost_round = 10000 , early_stopping_rounds=250, verbose_eval=50) y_test = lgbm.predict(X_test)

print(pd.DataFrame({ 'column':feature, 'importance':lgbm.feature_importance() }).sort_values(by='importance'))

test_df['result'] = y_testall_customer_submit = pd.merge(all_customer,test_df,on=['customer_id'],how='left',copy=False) all_customer_submit = all_customer_submit.sort_values(['customer_id']) all_customer_submit['customer_id'] = all_customer_submit['customer_id'].astype('int64') all_customer_submit['result'] = all_customer_submit['result'].fillna(0) all_customer_submit.to_csv('./DC_2019results.csv',index=False)

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