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我的主要目标是考虑更高价值的最新信息的功能。

因此，想法是通过新的原始转换“WeightTimeUntil”计算加权因子，然后转换原始“MultiplyNumeric”可以使用该加权因子来获得加权值。

我使用 Will Koehrsen 的演练演练作为数据和实体设置的起点。

因此我遇到了以下问题：

  1. featuretools 没有选择我想要实现的组合（见下文）
  2. 看起来 featuretools 没有选择组合，因为类型不匹配？！
  3. 通过更改我想要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
  4. 对于目标平等的客户，功能工具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相等贷款时，特征工具才使用正确的组合

这是“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))>
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1 回答 1

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这里的问题是这SUM(loans.loan_amount * loans.WEIGHT_TIME_UNTIL(loan_start))>是一个深度 3 功能，因为您正在堆叠Sum、MultiplyNumeric和WeightTimeUntil。您可以在此处的文档中阅读有关深度的更多信息。

您可以通过增加对 dfs 的调用中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])

第二种方法我更可取，因为它会创建您想要的功能，但整体功能较少。

于 2019-06-28T13:56:48.997 回答