我如何访问“每个估计器"?在 scikit-learn 管道中? [英] How can I access "each estimater " in scikit-learn pipelines?
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问题描述
如何在管道中访问Log"
?
pipelines = {
"Log": Pipeline(
[("scl", StandardScaler()), ("est", LogisticRegression(random_state=1))]
),
"Rf": Pipeline([("est", RandomForestClassifier(random_state=1))]),
"Rf_Pipeline": Pipeline(
[
("scl", StandardScaler()),
("reduct", PCA(n_components=10, random_state=1)),
("est", RandomForestClassifier(random_state=1)),
]
),
}
Pipelines.item(Log)
目前我得到:
NameError: name 'Log' is not defined
推荐答案
管道对象可以看作是字典.在您的情况下,您已将多个管道存储到字典中.要访问不同的密钥(管道),您只需使用 dict['key']
或 dict.get['key']
.
The pipeline objects can be viewed as dictionaries. In your case, you have stored multiple pipelines into a dictionary. To access the different keys (pipelines) you can simply use dict['key']
or dict.get['key']
.
- 对于第一级(子管道),只需使用
dict['key']
- 对于第二级(子管道内的步骤),您可以再次使用
named_steps
获取带有步骤的dict
,然后以相同的方式引用每个步骤.
- For the first level (sub pipelines), simply use
dict['key']
- For the second level, (steps inside sub pipelines), again, you can fetch the
dict
with steps usingnamed_steps
and then refer to each step the same way.
这是代码-
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
pipelines = {
"Log": Pipeline(
[("scl", StandardScaler()), ("est", LogisticRegression(random_state=1))]
),
"Rf": Pipeline([("est", RandomForestClassifier(random_state=1))]),
"Rf_Pipeline": Pipeline(
[
("scl", StandardScaler()),
("reduct", PCA(n_components=10, random_state=1)),
("est", RandomForestClassifier(random_state=1)),
]
),
}
first_subpipeline = pipelines['Log']
second_step_first_subpipeline = pipelines['Log'].named_steps['est']
print(first_subpipeline)
print(second_step_first_subpipeline)
Pipeline(steps=[('scl', StandardScaler()),
('est', LogisticRegression(random_state=1))])
LogisticRegression(random_state=1)
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