CODEWITHSUNDEEP
xgboostamazon-sagemaker
darekarsam
darekarsam

Reputation: 301

Feature Importance for XGBoost in Sagemaker

I have built an XGBoost model using Amazon Sagemaker, but I was unable to find anything which will help me interpret the model and validate if it has learned the right dependencies.

Generally, we can see Feature Importance for XGBoost by get_fscore() function in the python API (https://xgboost.readthedocs.io/en/latest/python/python_api.html) I see nothing of that sort in the sagemaker api(https://sagemaker.readthedocs.io/en/stable/estimators.html).

I know I can build my own model and then deploy that using sagemaker but I am curious if anyone has faced this problem and how they overcame it.

Thanks.

Upvotes: 6

Views: 5110

Answers (3)

Edward Kim
Edward Kim

Reputation: 51

Although you can write a custom script like rajesh and Lukas suggested and use XGBoost as a framework to run the script (see How to Use Amazon SageMaker XGBoost for how to use the "script mode"), SageMaker has recently launched SageMaker Debugger, which allows you to retrieve feature importance from XGBoost in real time.

Notebook demonstrates how to use SageMaker Debugger to retrieve feature importance.

Upvotes: 5

Lukas Vrabel
Lukas Vrabel

Reputation: 827

As of 2019-06-17, Sagemaker XGBoost model is stored on S3 in as archive named model.tar.gz. This archive consist of single pickled model file named xgboost-model.

To load the model directly from S3 without downloading, you can use the following code:

import s3fs
import pickle
import tarfile
import xgboost

model_path = 's3://<bucket>/<path_to_model_dir>/xgboost-2019-06-16-09-56-39-854/output/model.tar.gz'

fs = s3fs.S3FileSystem()

with fs.open(model_path, 'rb') as f:
    with tarfile.open(fileobj=f, mode='r') as tar_f:
        with tar_f.extractfile('xgboost-model') as extracted_f:
            xgbooster = pickle.load(extracted_f)

xgbooster.get_fscore()

Upvotes: 6

raj
raj

Reputation: 1213

SageMaker XGBoost currently does not provide interface to retrieve feature importance from the model. You can write some code to get the feature importance from the XGBoost model. You have to get the booster object artifacts from the model in S3 and then use the following snippet 

import pickle as pkl
import xgboost
booster = pkl.load(open(model_file, 'rb'))
booster.get_score()
booster.get_fscore()

Refer XGBoost doc for methods to get feature importance from the Booster object such as get_score() or get_fscore().

Upvotes: 4

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