213f4fa45a
* Fix loading old logit model. * Add a helper script for converting old pickle file. * Add version as a model parameter. * Remove the size check in R test to relax the size constraint. * Add missing R doc for passing linting. Run devtools. * Cleanup old model IO logic. * Test compatibility on CI. * Make the argument as required.
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
'''This is a simple script that converts a pickled XGBoost
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Scikit-Learn interface object from 0.90 to a native model. Pickle
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format is not stable as it's a direct serialization of Python object.
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We advice not to use it when stability is needed.
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'''
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import pickle
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import json
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import os
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import argparse
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import numpy as np
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import xgboost
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import warnings
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def save_label_encoder(le):
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'''Save the label encoder in XGBClassifier'''
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meta = dict()
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for k, v in le.__dict__.items():
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if isinstance(v, np.ndarray):
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meta[k] = v.tolist()
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else:
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meta[k] = v
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return meta
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def xgboost_skl_90to100(skl_model):
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'''Extract the model and related metadata in SKL model.'''
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model = {}
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with open(skl_model, 'rb') as fd:
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old = pickle.load(fd)
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if not isinstance(old, xgboost.XGBModel):
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raise TypeError(
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'The script only handes Scikit-Learn interface object')
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# Save Scikit-Learn specific Python attributes into a JSON document.
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for k, v in old.__dict__.items():
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if k == '_le':
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model[k] = save_label_encoder(v)
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elif k == 'classes_':
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model[k] = v.tolist()
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elif k == '_Booster':
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continue
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else:
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try:
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json.dumps({k: v})
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model[k] = v
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except TypeError:
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warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.')
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booster = old.get_booster()
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# Store the JSON serialization as an attribute
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booster.set_attr(scikit_learn=json.dumps(model))
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# Save it into a native model.
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i = 0
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while True:
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path = 'xgboost_native_model_from_' + skl_model + '-' + str(i) + '.bin'
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if os.path.exists(path):
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i += 1
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continue
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booster.save_model(path)
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break
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if __name__ == '__main__':
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assert xgboost.__version__ != '1.0.0', ('Please use the XGBoost version'
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' that generates this pickle.')
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parser = argparse.ArgumentParser(
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description=('A simple script to convert pickle generated by'
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' XGBoost 0.90 to XGBoost 1.0.0 model (not pickle).'))
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parser.add_argument(
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'--old-pickle',
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type=str,
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help='Path to old pickle file of Scikit-Learn interface object. '
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'Will output a native model converted from this pickle file',
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required=True)
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args = parser.parse_args()
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xgboost_skl_90to100(args.old_pickle)
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