Scikit learn 如何从Sklearn管道中提取特征重要性

我在Scikit Learn中构建了一个管道，它分为两个步骤：一个是构造特征，第二个是随机分类器

虽然我可以保存该管道，查看各个步骤和步骤中设置的各种参数，但我希望能够检查结果模型中的特征重要性

有可能吗？啊，是的

您可以列出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))

然后，您可以直接访问模型步骤：

---

pipeline.steps[1][1].feature\u importances\u

我写了一篇文章，介绍了如何进行这项工作

通常，对于管道，您可以访问

命名的\u步骤

参数。这将为您提供管道中的每个变压器。例如，对于这条管道：

model = Pipeline(
[
    ("vectorizer", CountVectorizer()),
    ("transformer", TfidfTransformer()),
    ("classifier", classifier),
])

我们可以通过执行

model.named_steps[“transformer”]访问各个功能步骤。获取_feature_names（）

这将从

tfidftranformer

返回功能名称列表。这一切都很好，但并没有涵盖很多用例，因为我们通常希望结合一些特性。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)])

在这里，我们使用特征联合和子管线来组合一些特征。要访问这些特性，我们需要按顺序显式调用每个命名步骤。例如，从内部管道获取TF-IDF功能，我们必须：

model.named_steps["union"].tranformer_list[3][1].named_steps["transformer"].get_feature_names()

这有点头疼，但它是可行的。通常我所做的是使用以下代码片段的变体来获得它。下面的代码只是将管道/要素联合集视为一棵树，并在运行时结合要素名称执行DFS

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 []

你也需要这个方法。它对单个转换进行操作，例如TFIDFvectorier，以获取名称。在SciKit Learn中，没有通用的

get\u功能\u name

，因此您必须针对不同的情况对其进行篡改。这是我试图为大多数用例做一些合理的事情

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]

---

要获取功能的名称，请查看pipe.steps[0][1]。获取功能名称（）这是一个不完整的答案。预处理和特征工程通常是管道的一部分。因此，您需要考虑到这一点。如果有多个步骤，那么一种方法是执行。对于OP的情况，这可能是

管道。命名为_步骤['predictor']。功能重要性u

。如何更改功能重要性类型？