transform原语可以很好地使用其他参数。这里有一个例子
def string_count(column, string=None):
'''
..note:: this is a naive implementation used for clarity
'''
assert string is not None, "string to count needs to be defined"
counts = [str(element).lower().count(string) for element in column]
return counts
def string_count_generate_name(self):
return u"STRING_COUNT(%s, %s)" % (self.base_features[0].get_name(),
'"' + str(self.kwargs['string'] + '"'))
StringCount = make_trans_primitive(
function=string_count,
input_types=[Categorical],
return_type=Numeric,
cls_attributes={
"generate_name": string_count_generate_name
})
es = ft.demo.load_mock_customer(return_entityset=True)
count_the_feat = StringCount(es['transactions']['product_id'], string="5")
fm, fd = ft.dfs(
entityset=es,
target_entity='transactions',
max_depth=1,
features_only=False,
seed_features=[count_the_feat])
输出:
^{pr2}$但是,如果我像这样修改并使其成为聚合原语:
def string_count(column, string=None):
'''
..note:: this is a naive implementation used for clarity
'''
assert string is not None, "string to count needs to be defined"
counts = [str(element).lower().count(string) for element in column]
return sum(counts)
def string_count_generate_name(self):
return u"STRING_COUNT(%s, %s)" % (self.base_features[0].get_name(),
'"' + str(self.kwargs['string'] + '"'))
StringCount = make_agg_primitive(
function=string_count,
input_types=[Categorical],
return_type=Numeric,
cls_attributes={
"generate_name": string_count_generate_name
})
es = ft.demo.load_mock_customer(return_entityset=True)
count_the_feat = StringCount(es['transactions']['product_id'], string="5")
我得到以下错误:
TypeError: new_class_init() missing 1 required positional argument: 'parent_entity'
featuretools是否支持带有附加参数的自定义聚合原语?在
这里的问题是缺少seed特性的参数。对于聚合原语,需要指定要在其上聚合的实体。在本例中,将聚合种子特性的构造更改为
将创建功能
^{pr2}$一如预期。该特性将给出每个会话id出现字符串“5”的频率
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