I think these are really easy to write for a single use-case, and hard
to be generally useful.
Why do you think pipelines make it hard? You know you can extract the
estimators from the steps, right?
def feature_importances_pipeline(pipe):
extractor = pipe.steps[0][1]
linear_model = pipe.steps[1][1]
return dict(zip(extractor.get_feature_names(), linear_model.coef_))
On 08/20/2015 02:47 AM, Christoph Sawade wrote:
Hey there!
In the last time, I used the linear models and the feature extraction
pipeline a lot. During the feature engineering process, I run again
and again in the situation, in which I wanted to inspect the
classifications and understand, which features had the highest
influence; this is important especially if one uses different feature
sources. In my case, I found it very helpful to have an inspection for
the model (independent of the instance) and one for the prediction of
a single example that gives some insights about the weights. E.g., an
inspection function for a binary linear classifier could return a list
of (feature name, weight)-pairs sorted by weight:
mdl = LogisticRegression()
# training, tuning,...
inspect(mdl)
/[(<sourcename_featname>, <weight>),...]/
predict_and_inspect(mdl, example)
/{/
/ 'probability': <prob_of_positive_class>,/
/ 'inspection':[(<sourcename_featname>, <model_weight*feature_weight>),...],
'label': <ground_truth_label>,
'prediction': <predicted_label>,
/
/ 'example_id': <id>/
/}/
The pipeline framework encapsulates the whole feature encoding, which is
typically very convenient. However, it is very hard to map the learned weights
back to the actual feature names.
Is there any easy way to do that or are there already initiatives to
build something like this? If not, do you think that this makes sense
in general? I know that this is tough to generalize over all possible
model classes (linear vs. kernel machines, number of classes, ...),
but I think it is worth to try it, since it is necessary to iterate on
a predictive model in practice.
Cheers, Christoph
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