指定原始选项的 Featuretools 文档是错误的？

Question

The documentation 说：

指定单个基元

单个基元或基元组的选项由 DFS 的 primitive_options 参数设置。此参数将任何所需选项映射到特定原语。在选项冲突的情况下，在此级别设置的选项将覆盖在整个 DFS 运行级别设置的选项，并且包含选项将始终优先于其忽略对应项。

但是，我发现这不是真的，忽略选项实际上优先于包含对应项。

以下是我将用来演示声明行为的设置。它是一个有一个祖父母 (gp)、两个父母 (p1,p2) 和一个孩子 (c) 到一个父母 (p1) 的实体集：

import pandas as pd
import featuretools as ft
from featuretools import variable_types as vt

# # Creating Relational Dataset
# ## Grand Parent 
df_gp = pd.DataFrame({'gp_ind':['a','b'],
                     'gp_ncol1':[1,2],'gp_ncol2':[3,4],
                     'gp_ccol1':['x','y'],'gp_ccol2':['p','q'],
                    'gp_time_col1':pd.to_datetime(['20-01-2020','20-01-2019']),
                    'gp_time_ind':pd.to_datetime(['20-01-2021','20-01-2020'])})

# ## Parent 1
df_p1 = pd.DataFrame({'p1_ind':['a1','a2','b1'],
                     'p1_id': ['a','a','b'],
                     'p1_ncol1':[1,2,3],'p1_ncol2':[3,4,5],
                     'p1_ccol1':['x','y','z'],'p1_ccol2':['p','q','r'],
                      'p1_id1' : ['t','t','u'],
                    'p1_time_col1':pd.to_datetime(['16-01-2020','11-12-2019','16-01-2019'],format="%d-%m-%Y"),
                    'p1_time_ind':pd.to_datetime(['15-01-2021','10-12-2020','15-01-2020'],format="%d-%m-%Y")})

# ## Parent 2
df_p2 = pd.DataFrame({'p2_ind':['a1_','a2_','b1_'],
                     'p2_id': ['a','a','b'],
                     'p2_ncol1':[1,2,3],'p2_ncol2':[3,4,5],
                     'p2_ccol1':['x','y','z'],'p2_ccol2':['p','q','r'],
                    'p2_time_col1':pd.to_datetime(['18-01-2020','13-12-2019','18-01-2019'],format="%d-%m-%Y"),
                    'p2_time_ind':pd.to_datetime(['17-01-2021','12-12-2020','17-01-2020'],format="%d-%m-%Y")})

# ## Child 
df_c = pd.DataFrame({'c_ind':['a1_1','a1_2','a2_1','a2_2','a2_3','b1_1'],
                     'c_id': ['a1','a1','a2','a2','a2','b1'],
                     'c_ncol1':[1,2,3,4,5,6],'c_ncol2':[3,4,5,6,7,8],
                     'c_ccol1':['x','y','z','a','b','c'],'c_ccol2':['p','q','r','s','t','u'],
                    'c_time_col1':pd.to_datetime(['13-01-2020','10-12-2019','8-12-2019','5-11-2019','2-10-2019','13-01-2019'],format="%d-%m-%Y"),
                    'c_time_ind':pd.to_datetime(['10-01-2021','5-12-2020','9-12-2020','6-11-2020','3-10-2019','12-01-2020'],format="%d-%m-%Y")})

# # Creating Entityset
es = ft.EntitySet(id='experimentation')

# ## Adding entities
# ### Adding gp
vt_gp = {'gp_ind':vt.Index,
        'gp_ncol1':vt.Numeric,
        'gp_ncol2':vt.Numeric,
        'gp_ccol1':vt.Categorical,
        'gp_ccol2':vt.Categorical,
        'gp_time_col1':vt.Datetime,
        'gp_time_ind':vt.DatetimeTimeIndex}
es.entity_from_dataframe(entity_id='gp',dataframe=df_gp,index='gp_ind',variable_types=vt_gp,
                time_index='gp_time_ind')

# ### Adding p1 
vt_p1 = {'p1_ind':vt.Index,
        'p1_id':vt.Id,
        'p1_id1' : vt.Id,
        'p1_ncol1':vt.Numeric,
        'p1_ncol2':vt.Numeric,
        'p1_ccol1':vt.Categorical,
        'p1_ccol2':vt.Categorical,
        'p1_time_col1':vt.Datetime,
        'p1_time_ind':vt.DatetimeTimeIndex}
es.entity_from_dataframe(entity_id='p1',dataframe=df_p1,index='p1_ind',variable_types=vt_p1,
                time_index='p1_time_ind')

# ### Adding p2
vt_p2 = {'p2_ind':vt.Index,
        'p2_id':vt.Id,
        'p2_ncol1':vt.Numeric,
        'p2_ncol2':vt.Numeric,
        'p2_ccol1':vt.Categorical,
        'p2_ccol2':vt.Categorical,
        'p2_time_col1':vt.Datetime,
        'p2_time_ind':vt.DatetimeTimeIndex}
es.entity_from_dataframe(entity_id='p2',dataframe=df_p2,index='p2_ind',variable_types=vt_p2,
                time_index='p2_time_ind')

# ### Adding c
vt_c = {'c_ind':vt.Index,
        'c_id':vt.Id,
        'c_ncol1':vt.Numeric,
        'c_ncol2':vt.Numeric,
        'c_ccol1':vt.Categorical,
        'c_ccol2':vt.Categorical,
        'c_time_col1':vt.Datetime,
        'c_time_ind':vt.DatetimeTimeIndex}
es.entity_from_dataframe(entity_id='c',dataframe=df_c,index='c_ind',variable_types=vt_c,
                time_index='c_time_ind')

# ## Adding Relationships
r_gp_p1 = ft.Relationship(es['gp']['gp_ind'],es['p1']['p1_id'])
r_gp_p2 = ft.Relationship(es['gp']['gp_ind'],es['p2']['p2_id'])
r_p1_c = ft.Relationship(es['p1']['p1_ind'],es['c']['c_id'])
es.add_relationships([r_gp_p1,r_gp_p2,r_p1_c])

# ## Create Cutoff Times
cutoff_times = df_gp.loc[:,['gp_ind','gp_time_ind']].copy(deep=True)

# ## add interesting values
es['p1']['p1_ccol1'].interesting_values = es['p1'].df['p1_ccol1'].unique()[0:1]
es['c']['c_ccol1'].interesting_values = es['c'].df['c_ccol1'].unique()[0:1]

# ## Add last time index 
es.add_last_time_indexes()

# ## Plotting entityset 
es.plot()

现在我在这个实体集上运行以下dfs：

我在ignore_entities 和include_entities 键中都包含p1。这样，我向dfs 传达了关于是否在特征创建过程中包含p1 实体的冲突命令。

预期行为： include_entities 覆盖 ignore_entities 和实体 p1 上的变量应该进行

所见行为： ignore_entities 覆盖 include_entities 并且不会生成 p1 上的变量

agg_primitives = ['sum']
where_primitives = ['sum']

primitive_options = {}
primitive_options[('sum',)] = {}

primitive_options[('sum',)]['ignore_entities'] = ['p1'] 
primitive_options[('sum',)]['include_entities'] = ['p1'] 

features = ft.dfs(entityset=es,target_entity='gp', cutoff_time=cutoff_times,
                  agg_primitives=agg_primitives,features_only=True,max_depth=2,
                  where_primitives = where_primitives,
primitive_options=primitive_options,trans_primitives=[])

features

output:
[<Feature: gp_ncol1>,
 <Feature: gp_ncol2>,
 <Feature: gp_ccol1>,
 <Feature: gp_ccol2>]

p1 上没有与文档中所述内容相悖的功能

我在这里遗漏了什么还是文档实际上是错误的，我应该明白ignore_entities 会覆盖include_entities

Answer 1

这是一个错误，您可以在此处跟踪建议的修复：https://github.com/alteryx/featuretools/pull/1518