How to extract feature importances from an Sklearn pipeline

Question

I\'ve built a pipeline in Scikit-Learn with two steps: one to construct features, and the second is a RandomForestClassifier.

While I can save that pipeline, look at

Answer 1

Ah, yes it is.

You list identify the step where you want to check the estimator:

For instance:

pipeline.steps[1]

Which returns:

('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))

You can then access the model step directly:

pipeline.steps[1][1].feature_importances_

Answer 2

I wrote an article on doing this in general you can find here.

In general for a pipeline you can access the named_steps parameter. This will give you each transformer in a pipeline. So for example for this pipeline:

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

we could access the individual feature steps by doing model.named_steps["transformer"].get_feature_names() This will return the list of feature names from the TfidfTransformer. This is all fine and good but doesn't really cover many use cases since we normally want to combine a few features. Take this model for example:

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)])

Here we combine a few features using a feature union and a subpipeline. To access these features we'd need to explicitly call each named step in order. For example getting the TF-IDF features from the internal pipeline we'd have to do:

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

That's kind of a headache but it is doable. Usually what I do is use a variation of the following snippet to get it. The below code just treats sets of pipelines/feature unions as a tree and performs DFS combining the feature_names as it goes.

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 []

You'll also need this method. Which operates on individual transformations, things like the TfidfVectorizer, to get the names. In SciKit-Learn there isn't a universal get_feature_names so you have to kind of fudge it for each different case. This is my attempt at doing something reasonable for most use cases.

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]