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I am looking for solutions to quantize sklearn models. I am specifically looking for XGBoost models.

I did find solutions to quantize pytorch and tensorflow models but nothing on sklearn.

Solutions tried:

Converted sklearn model to ONNX and then tried to quantize ONNX model, but that didn't work either. Here is the link to the bug.

Any pointers or solutions can be shared, it would be of great help.

pratsbhatt
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1 Answers1

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Someone had answered the question in your link bug.

Try not to add a final node ZipMap by the 'zipmap' option:

onx = convert_sklearn(clr, initial_types=initial_type,
                      options={'zipmap': False})

I'm interested to know if that works for you?

BTW, you can use onnxmltools to convert a XGBoost model to ONNX according to this.

the sample code:

import onnx
import onnxmltools
from onnxmltools.convert.common.data_types import FloatTensorType
from xgboost import XGBClassifier

clf = XGBClassifier()

# fit the classifier...

onnx_model_path = "xgb_classifier.onnx"
initial_type = [('float_input', FloatTensorType([None, num_features]))]
onnx_model = onnxmltools.convert.convert_xgboost(clf, initial_types=initial_type, target_opset=10)
onnx.save(onnx_model, onnx_model_path)

Note that:

  • Model must be trained using the scikit-learn API of xgboost
  • The training data passed to XGBClassifier().fit() must not have feature names associated with it. For example, if your training data is a DataFrame called df, which has column names, you will need to use a representation without column names (i.e. df.values) when training.
SC Chen
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