python生成所需特征参数

def make_phone(num):
    lt=[]
    ahead=np.random.randint(125,195,num)
    follow=np.random.randint(0,99999999,num)
    for a,f in zip(ahead,follow):
        k=f'{a}{f:0>8}'
        lt.append(k)
    return lt
    
make_phone(100) 
# ----- 结果 -----
['18262627913',
 '15524909880',
 '16417080083',
    ……
 '19480918346',
 '14774805532',
 '12893994298',
 '14275982379',]

def make_n_time(num):
    # 0~23时间的权值
    time_rights=[3.2,2.4,1.8,0.7,0.4,0.35,0.56,0.9,1.2,1.8,2,2.5,1.8,2.5,2.6,2.8,2.5,3.1,2.4,3.8,4.5,4.2,3.6,3.3] 
    # 转换为百分比
    time_rights=[rights/sum(time_rights) for rights in time_rights]
    times=[]
    for i in range(num):
        time_get=np.random.choice(range(24),p=time_rights)
        times.append(f'{time_get:0>2}:{(np.random.randint(0,60)):0>2}:{np.random.randint(0,60):0>2}')
    return times

def make_n_date(mnt,num,ver_date_start,ver_date_end):
    date_begin=f"2024-{mnt:0>2}-01"
    # 日期范围列表
    date_list=pd.date_range(date_begin,pd.Timestamp(date_begin)+pd.offsets.MonthEnd(0))
    # 这个月有多少天
    daysofmonth=(pd.Timestamp(date_begin)+pd.offsets.MonthEnd(0)-pd.to_datetime(date_begin)).days+1
    # 每月初始权值
    rights=np.ones(daysofmonth)
    
    ll=pd.Series(date_list).dt.dayofweek
    week_val=ll.values # 星期几
    week_index=ll.index.values # 对应位置
    # 找出周五~周日的日志
    # 按照权值随机赋予权值大小
    for indx,val in zip(week_index,week_val):
        if val in(6,5,4):
            rights[indx]=(np.random.choice([1,2,3,4,5],1,p=[0.1,0.3,0.3,0.2,0.1]))
    # 找到符合日期区间的标签
    target_index=np.argwhere((date_list>=ver_date_start) & (date_list<=ver_date_end))
    # 找到目标区间的前三天的标签
    target_index=target_index.flatten()[:3]
    # 求周末时期的权重
    avg_right=np.mean(rights)
    # 目标区间的权重分贝为周末权重的10倍、8倍、以及3倍，依次衰减
    rights[target_index]=[avg_right*10,avg_right*8,avg_right*3]
    # 将权值化为为百分比
    rights=rights/sum(rights)
    # 抽取num个结果，并将返回值从datetime64类型改为str
    date_real=np.random.choice(date_list.astype(str),num,p=rights)
    return date_real

# usr_cnt 用户总数
# free_usr_rate 用户非付费率
# mean_times 用户平均消费次数
# std_dev 用户平均消费次数的标准差
def make_all_usr_amount(usr_cnt,free_usr_rate,mean_times,std_dev):
    # 将付费用户插入总表
    all_usr_amount=list(np.zeros(usr_cnt)) # 所有用户，付费初始为0
    # 消费详细金额
    all_detail_comsume=list(np.zeros(usr_cnt))
    # 消费日期
    all_datetime_comsume=[' ' for _ in range(usr_cnt)]
    
    pay_usr_num=usr_cnt-int(usr_cnt*free_usr_rate)
    # 按照标准正态分布的模板 mean=0 std=1
    scaled_normal = np.random.normal(0, 1, pay_usr_num)  
        # 缩放和平移这个分布到标准分布的期望的范围  
    mean=mean_times
    std_dev=std_dev
    gacha = mean*10 + std_dev * scaled_normal # 避免出现负数先放大10倍
    gacha=gacha/10 # 还原原来大小
    gacha = [max(x,1) for x in gacha] # 付费用户至少1次消费
    gacha = np.round(gacha).astype(int)
    std_amount=[30,68,98,128,326,648] # 充钱档次
    usr_amount_lt=[] 
    detail_comsume=[]
    
    # 求付费用户每次抽取对应的时间与金额gacha为所有用户的抽取次数
    # 抽卡次数等于3为阈值，分别设置消费次数的权值
    for i in gacha:
        if i<3:
            consum_right=[50,10,5,5,5,5]
        else:
            consum_right=[5,5,10,20,30,40]
        consum_right=[r/sum(consum_right) for r in consum_right]

        amount_lt=np.random.choice(std_amount,i,p=consum_right)
        # 消费列表
        detail_comsume.append(amount_lt)
       # 消费总金额
        total_amount=sum(amount_lt)
        usr_amount_lt.append(total_amount)
        
    # 付费用户随机插入其中的位置，replace=False不放回，根据随机标签决定位置分布
    # 用pop从原数据列表中抽取数据
    insert_index=list(np.random.choice(range(usr_cnt),pay_usr_num,replace=False)) 
    for idx in insert_index:
        all_usr_amount[idx]=usr_amount_lt.pop()
        all_detail_comsume[idx]=detail_comsume.pop()

    # 返回为DataFrame
    df=pd.DataFrame({'usr_amount':all_usr_amount,'detail_comsume':all_detail_comsume})
    df.reset_index(inplace=True)
    df.rename(columns={'index':'uid'},inplace=True) # 生成uid
    df['uid']=df['uid']+10000000 # uid偏移
    df.uid=df.uid.map(str) # 修改格式类型
        
    return df

def make_ages(mean_amount,amount_list):
    right1=np.ones(len(np.arange(8,12)))*2

    right2=np.ones(len(np.arange(12,16)))*3

    right3=np.linspace(3,15,len(range(16,22)))

    right4=np.linspace(15,4,len(range(22,35)))

    right5=np.linspace(4,0.5,len(range(35,45)))

    right6=np.linspace(0.1,0,len(range(45,101)))
	
    # 获取总的各年龄段的权值
    rights=np.concatenate([right1,right2,right3,right4,right5,right6])
    rights=rights/sum(rights)
    # 初始年龄列表
    age_list=np.random.choice(range(8,101),len(amount_list),p=rights)

    # 消费力低的idx
    index0=amount_list[(amount_list<mean_amount*0.2)].index
    # 高于平均值的idx
    index1=amount_list[(amount_list>mean_amount) & (amount_list<mean_amount*2)].index
    # 平均值2倍以上的idx
    index2=amount_list[amount_list>mean_amount*2].index
    
    # 根据付费年征修改年龄
    # 未成付费能力弱，拉高抽取到低龄的可能性
    rights=np.concatenate([right1*5,right2*3,right3,right4,right5,right6])
    rights=rights/sum(rights)
    for idx in index0:
        age_list[idx] = np.random.choice(range(8,101),p=rights)
    
    # 中学付费欲望较强，拉高抽取到中学的可能性
    rights=np.concatenate([right1,right2,right3*1.8,right4*1.5,right5,right6])
    rights=rights/sum(rights)
    for idx in index1:
        age_list[idx] = np.random.choice(range(8,101),p=rights)
    
    # 中学到青年付费欲望最强，拉高抽取到青年的可能性
    rights=np.concatenate([right1,right2,right3*1.2,right4*2,right5,right6])
    rights=rights/sum(rights)
    for idx in index2:
        age_list[idx] = np.random.choice(range(8,101),p=rights)
    return age_list