如何使用 MultiplyNumeric 将日期转换为权重和相同的值 table？

Question

我的主要目标是考虑具有更高价值的最新信息的功能。

所以，这个想法是通过一个新的原语 t运行sformation "WeightTimeUntil" 计算一个权重因子，之后可以被 t运行sformation 原语使用 "MultiplyNumeric" 获取加权值。

我使用 Will Koehrsen 的演练 walkthrough 作为数据和实体设置的起点。

因此我运行陷入以下问题：

featuretools 没有选择我想要实现的组合（见下文）
featuretools 似乎因为类型不匹配而没有选择组合？！
通过更改我想乘以权重因子的值的类型，我设法获得了正确的组合，但不是正确的目标
对于target equal client，featuretools根本没有选择我想要得到的组合。只有当我使用日期和值是列的目标均等贷款时，featuretools 使用正确的组合

这里是"WeightTimeUntil"原语

的代码

def weight_time_until(array, time):
    diff = pd.DatetimeIndex(array) - time
    s = np.floor(diff.days/365/0.5)
    aWidth = 9
    a = math.log(0.1) / ( -(aWidth -1) )

    w = np.exp(-a*s) 

    return w


    WeightTimeUntil = make_trans_primitive(function=weight_time_until,
                                 input_types=[Datetime],
                                 return_type=Numeric,
                                 uses_calc_time=True,
                                 description="Calculates weight time until the cutoff time",
                                 name="weight_time_until")

这里是DFS执行代码：

features, feature_names = ft.dfs(entityset = es, target_entity = 'clients', 
                                 agg_primitives = ['sum'],
                                 trans_primitives = [WeightTimeUntil, MultiplyNumeric])

这里是功能列表：

 <Feature: income>,
 <Feature: credit_score>,
 <Feature: join_month>,
 <Feature: log_income>,
 <Feature: SUM(loans.loan_amount)>,
 <Feature: SUM(loans.rate)>,
 <Feature: SUM(payments.payment_amount)>,
 <Feature: WEIGHT_TIME_UNTIL(joined)>,
 <Feature: join_month * log_income>,
 <Feature: income * log_income>,
 <Feature: income * join_month>,
 <Feature: credit_score * join_month>,
 <Feature: credit_score * log_income>,
 <Feature: credit_score * income>,
 <Feature: SUM(loans.WEIGHT_TIME_UNTIL(loan_start))>,
 <Feature: SUM(loans.WEIGHT_TIME_UNTIL(loan_end))>,
 <Feature: SUM(loans.loan_amount * rate)>,
 <Feature: income * SUM(loans.loan_amount)>,
 <Feature: credit_score * SUM(loans.loan_amount)>,
 <Feature: log_income * SUM(payments.payment_amount)>,
 <Feature: log_income * WEIGHT_TIME_UNTIL(joined)>,
 <Feature: income * SUM(payments.payment_amount)>,
 <Feature: join_month * SUM(loans.rate)>,
 <Feature: income * SUM(loans.rate)>,
 <Feature: join_month * SUM(loans.loan_amount)>,
 <Feature: SUM(loans.rate) * SUM(payments.payment_amount)>,
 <Feature: credit_score * WEIGHT_TIME_UNTIL(joined)>,
 <Feature: SUM(loans.rate) * WEIGHT_TIME_UNTIL(joined)>,
 <Feature: income * WEIGHT_TIME_UNTIL(joined)>,
 <Feature: log_income * SUM(loans.loan_amount)>,
 <Feature: SUM(loans.loan_amount) * WEIGHT_TIME_UNTIL(joined)>,
 <Feature: SUM(loans.loan_amount) * SUM(payments.payment_amount)>,
 <Feature: credit_score * SUM(loans.rate)>,
 <Feature: log_income * SUM(loans.rate)>,
 <Feature: credit_score * SUM(payments.payment_amount)>,
 <Feature: SUM(payments.payment_amount) * WEIGHT_TIME_UNTIL(joined)>,
 <Feature: join_month * WEIGHT_TIME_UNTIL(joined)>,
 <Feature: SUM(loans.loan_amount) * SUM(loans.rate)>,
 <Feature: join_month * SUM(payments.payment_amount)>

我期待这样的事情：

SUM(loans.loan_amount * loans.WEIGHT_TIME_UNTIL(loan_start))>

Answer 1

这里的问题是 SUM(loans.loan_amount * loans.WEIGHT_TIME_UNTIL(loan_start))> 是深度 3 特征，因为您正在堆叠 Sum、MultiplyNumeric 和 WeightTimeUntil。您可以在文档 here.

中阅读有关深度的更多信息

您可以通过像这样增加对 dfs 的调用的允许深度来解决此问题

features, feature_names = ft.dfs(entityset = es, target_entity = 'clients', 
                                 agg_primitives = ['sum'],
                                 max_depth=3,
                                 trans_primitives = [WeightTimeUntil, MultiplyNumeric])

另一种方法是将您的特征作为种子特征提供，不计入最大深度。你可以这样做

seed_features=[ft.Feature(es["loans"]["loan_start"], primitive=WeightTimeUntil)]

features, feature_names = ft.dfs(entityset = es, target_entity = 'clients', 
                                 agg_primitives = ['sum'],
                                 seed_features=seed_features,
                                 trans_primitives = [MultiplyNumeric])

我更喜欢第二种方法，因为它会创建您想要的功能，但总体上功能较少。

如何使用 MultiplyNumeric 将日期转换为权重和相同的值 table？

How do you use MultiplyNumeric for a date transformed to a weight and a value of the same table?

python

featuretools