Using XGBClassifier.Predict after load_model causes 'XGBClassifier' object has no attribute '_le'

[paulbir]

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Environment info

Operating System: Windows 10

Compiler: Unofficial windows binaries

Package used (python/R/jvm/C++): python

xgboost version used: 0.6

If you are using python package, please provide

1. The python version and distribution: version - 2.7.12. distribution - Anaconda
2. The command to install xgboost if you are not installing from source: python setup.py install

Steps to reproduce

    clf = xgb.XGBClassifier()
    booster = xgb.Booster()
    booster.load_model('bin_model')
    clf._Booster = booster

y_pred = clf.predict(X_test)
3.
AttributeError: 'XGBClassifier' object has no attribute '_le'
in "predict" function on line 485:
return self._le.inverse_transform(column_indexes)

This happens because _le attribute is set in fit function:
self._le = XGBLabelEncoder().fit(y)

If you don't call fit, _le is not set, so exception is reaised.

[mangolzy]

Environment info

Operating System: linux based on docker

Package used (python/R/jvm/C++): python

xgboost version used: 0.6

If you are using python package, please provide

The python version and distribution: version - 2.7.5

Steps to reproduce

clf = xgb.XGBClassifier()
booster = xgb.Booster()
booster.load_model('bin_model')
clf._Booster = booster

y_pred = clf.predict(X_test)
3.
AttributeError: 'XGBClassifier' object has no attribute '_le'
in "predict" function on line 485:
return self._le.inverse_transform(column_indexes)

This happens because _le attribute is set in fit function:
self._le = XGBLabelEncoder().fit(y)

so what's the solution, basically, if we call load_model, there is no need to call fit, and according to the former post, that's the cause....

[paulbir]

so what's the solution, basically, if we call load_model, there is no need to call fit, and according to the former post, that's the cause

Maybe check if self._le is instantiated in both fit and predict. If not, then create it.

[mangolzy]

oh, but how to instantiate the parameter inside xgboost predefined function..

I think the problem is because we use XGBClassifier which is sklearn API in the first place. but save_model and dump_model and load_model is actually xgboost's native function.. they are not compatible, and in fact we don't have the corresponding model saving model inside sklearn...... so some use Pickle etc. the third party packages

[paulbir]

I had to use a workaround.

import xgboost as xgb
from sklearn.preprocessing import LabelEncoder

booster = xgb.Booster()
booster.load_model('bin_model')
clf._Booster = booster
clf._le = LabelEncoder().fit(y_test)
y_pred_proba = clf.predict_proba(X_test)
y_pred = clf.predict(X_test)

But assigning _le can be done inside predict function. And it would help avoiding misleading exceptions.

[mangolzy]

but what if we don't have y_test, the normal scene when we do real test....?
does it mean that i could use some fake y_test value just to complete the initialization schedule?

[paulbir]

Can expected classed be extracted from the booster?

[ghost]

@mangolzy If you don't have the y_test, you just need the classes:

clf._le = LabelEncoder().fit(['label1', 'label2'])