能否手动定义种子特征的where子句？ft.dfs参数相关技术咨询

Great questions! Let's break these down clearly for you:

You can directly construct seed features with custom where conditions using Featuretools' core APIs. Here are two straightforward approaches:

• Using the ft.Feature class: Explicitly define the feature and attach your where condition. For example, if you have a transactions entity with amount and is_return columns, and you want a seed feature for "amount of refund transactions":

  import featuretools as ft
  
  # Assume your entityset `es` is already loaded
  transactions = es["transactions"]
  
  # Manually create the seed feature with where clause
  refund_amount = ft.Feature(
      transactions["amount"],
      where=transactions["is_return"] == 1,
      name="refund_amount"
  )
  

• Inline conditional filtering: A more concise way is to filter the entity column directly with your condition, then rename the feature:

  refund_amount = transactions["amount"][transactions["is_return"] == 1].rename("refund_amount")
  

Either way, you'll get a seed feature with your custom where clause ready to use in your feature engineering pipeline.

Absolutely! The where_primitives parameter tells Featuretools to automatically generate features with where conditions based on entity variables, but this doesn't restrict you from adding your own manually defined seed features with custom where clauses.

Here's how to combine both approaches:

1. First, create your custom seed features with where clauses as shown above.
2. Pass these features into the seed_features parameter when calling ft.dfs. The DFS process will use your manual seed features as a base, alongside any auto-generated features from where_primitives, to build more complex features.

Example code:

# Manually define your seed feature with where clause
refund_amount = transactions["amount"][transactions["is_return"] == 1].rename("refund_amount")

# Run DFS with both manual seed features and auto where-based primitives
features, feature_defs = ft.dfs(
    entityset=es,
    target_entity="customers",
    seed_features=[refund_amount],
    where_primitives=["mean", "sum"],  # Auto-generate aggregated features with where conditions
    agg_primitives=["mean", "sum"]
)

This gives you the best of both worlds: automated feature generation for common patterns, plus tailored seed features that fit your specific business logic.

内容的提问来源于stack exchange，提问作者Jeff Hernandez