我在Scikit-Learn中构建了一个流水线,其中包括两个步骤:第一个步骤是构造特征,第二个步骤是一个RandomForestClassifier。
虽然我可以保存这个流水线,查看各个步骤和步骤中设置的各种参数,但我想能够检查结果模型中的特征重要性。
这可行吗?
我在Scikit-Learn中构建了一个流水线,其中包括两个步骤:第一个步骤是构造特征,第二个步骤是一个RandomForestClassifier。
虽然我可以保存这个流水线,查看各个步骤和步骤中设置的各种参数,但我想能够检查结果模型中的特征重要性。
这可行吗?
啊,是的,没错。
您需要确定要检查估计器的步骤:
例如:
pipeline.steps[1]
返回:
('predictor',
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=2,
oob_score=False, random_state=None, verbose=0,
warm_start=False))
您可以直接访问模型的一步:pipeline.steps[1][1].feature_importances_
我写了一篇关于如何在通用情况下实现这一点的文章,你可以在这里找到。
通常来说,在管道中您可以访问named_steps
参数。 这将为您提供管道中的每个转换器。例如,对于此管道:
model = Pipeline(
[
("vectorizer", CountVectorizer()),
("transformer", TfidfTransformer()),
("classifier", classifier),
])
model.named_steps["transformer"].get_feature_names()
访问各个特征步骤。这将返回来自TfidfTransformer
的特征名称列表。这很好,但并不真正涵盖许多用例,因为我们通常想要组合几个特征。以这个模型为例:model = Pipeline([
("union", FeatureUnion(transformer_list=[
("h1", TfidfVectorizer(vocabulary={"worst": 0})),
("h2", TfidfVectorizer(vocabulary={"best": 0})),
("h3", TfidfVectorizer(vocabulary={"awful": 0})),
("tfidf_cls", Pipeline([
("vectorizer", CountVectorizer()),
("transformer", TfidfTransformer())
]
))
])
),
("classifier", classifier)])
这里我们使用特征联合和子管道组合了一些特征。要访问这些特征,我们需要按顺序显式调用每个命名步骤。例如,如果要从内部流水线中获取TF-IDF特征,我们需要执行以下操作:
model.named_steps["union"].tranformer_list[3][1].named_steps["transformer"].get_feature_names()
这有点麻烦,但是可行。通常我使用以下代码段的变体来获取它。下面的代码将管道/特征联合集合视为一棵树,并执行DFS组合feature_names。
from sklearn.pipeline import FeatureUnion, Pipeline
def get_feature_names(model, names: List[str], name: str) -> List[str]:
"""Thie method extracts the feature names in order from a Sklearn Pipeline
This method only works with composed Pipelines and FeatureUnions. It will
pull out all names using DFS from a model.
Args:
model: The model we are interested in
names: The list of names of final featurizaiton steps
name: The current name of the step we want to evaluate.
Returns:
feature_names: The list of feature names extracted from the pipeline.
"""
# Check if the name is one of our feature steps. This is the base case.
if name in names:
# If it has the named_steps atribute it's a pipeline and we need to access the features
if hasattr(model, "named_steps"):
return extract_feature_names(model.named_steps[name], name)
# Otherwise get the feature directly
else:
return extract_feature_names(model, name)
elif type(model) is Pipeline:
feature_names = []
for name in model.named_steps.keys():
feature_names += get_feature_names(model.named_steps[name], names, name)
return feature_names
elif type(model) is FeatureUnion:
feature_names= []
for name, new_model in model.transformer_list:
feature_names += get_feature_names(new_model, names, name)
return feature_names
# If it is none of the above do not add it.
else:
return []
您还需要这个方法。它作用于单个转换,例如TfidfVectorizer,以获取名称。在SciKit-Learn中没有通用的get_feature_names
,因此您必须为每种不同情况进行调整。这是我尝试为大多数用例做出合理处理的方法。
def extract_feature_names(model, name) -> List[str]:
"""Extracts the feature names from arbitrary sklearn models
Args:
model: The Sklearn model, transformer, clustering algorithm, etc. which we want to get named features for.
name: The name of the current step in the pipeline we are at.
Returns:
The list of feature names. If the model does not have named features it constructs feature names
by appending an index to the provided name.
"""
if hasattr(model, "get_feature_names"):
return model.get_feature_names()
elif hasattr(model, "n_clusters"):
return [f"{name}_{x}" for x in range(model.n_clusters)]
elif hasattr(model, "n_components"):
return [f"{name}_{x}" for x in range(model.n_components)]
elif hasattr(model, "components_"):
n_components = model.components_.shape[0]
return [f"{name}_{x}" for x in range(n_components)]
elif hasattr(model, "classes_"):
return classes_
else:
return [name]
pipeline.named_steps['predictor'].feature_importances_
。 - edesz