Loading XGBoost model from pickle file. Error: 'XGBClassifier' object has no attribute 'use_label_encoder'

user_2140489 0
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I am trying to load a serialized xgboost model from a pickle file.

import pickle
def load_pkl(fname):
    with open(fname, 'rb') as f:
        obj = pickle.load(f)
    return obj

model = load_pkl('model_0_unrestricted.pkl')

while printing the model object, I am getting the following error in linux(AWS Sagemaker Notebook)

    ~/anaconda3/envs/python3/lib/python3.6/site-packages/xgboost/sklearn.py in get_params(self, deep)
    436             if k == 'type' and type(self).__name__ != v:
    437                 msg = 'Current model type: {}, '.format(type(self).__name__) + \
--> 438                       'type of model in file: {}'.format(v)
    439                 raise TypeError(msg)
    440             if k == 'type':

~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/base.py in get_params(self, deep)
    193         out = dict()
    194         for key in self._get_param_names():
--> 195             value = getattr(self, key)
    196             if deep and hasattr(value, 'get_params'):
    197                 deep_items = value.get_params().items()

AttributeError: 'XGBClassifier' object has no attribute 'use_label_encoder'

Can you please help to fix the issue?

It is working fine in my local mac.

Ref: xgboost:1.4.1 installation log (Mac)

    Collecting xgboost
  Downloading xgboost-1.4.1-py3-none-macosx_10_14_x86_64.macosx_10_15_x86_64.macosx_11_0_x86_64.whl (1.2 MB)

But not working on AWS

Ref: xgboost:1.4.1 installation log (SM Notebook, linux machine)

Collecting xgboost
  Using cached xgboost-1.4.1-py3-none-manylinux2010_x86_64.whl (166.7 MB)

Thanks

xgboost data-science amazon-sagemaker machine-learning python
3 Answers

Looks like you upgraded xgboost. You may consider downgrading to 1.2.0 by:

pip install xgboost==1.2.0
user_82103900

I suspect the pickled model you are loading in was modified in someway to have that additional method prior to being saved. Either that or as @vbhatt said, you may be modifying some aspect of your classifier prior to loading it in. This has happened to me before when using custom models in Pytorch Lightning.

If you haven't modified the base model at all, please ensure that you are using the same version from within the notebook as well, could be the venv in the notebook has a different version?

user_64111530

I tried testing on notebook running on ubuntu, it seems to work fine, however can you check how are you initializing your classifier ? This is what I tried :

import numpy as np
import pickle
from scipy.stats import uniform, randint

from sklearn.datasets import load_breast_cancer, load_diabetes, load_wine
from sklearn.metrics import auc, accuracy_score, confusion_matrix, mean_squared_error
from sklearn.model_selection import cross_val_score, GridSearchCV, KFold,RandomizedSearchCV, train_test_split

import xgboost as xgb
cancer = load_breast_cancer()
X = cancer.data
y = cancer.target
xgb_model = xgb.XGBClassifier(objective="binary:logistic", random_state=45)
xgb_model.fit(X, y)
pickle.dump(xgb_model, open("xgb_model.pkl", "wb"))

Load the model back using your function and output it :

def load_pkl(fname):
    with open(fname, 'rb') as f:
        obj = pickle.load(f)
    return obj

model = load_pkl('xgb_model.pkl')
model

Below is the output :

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
          colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
          importance_type='gain', interaction_constraints='',
          learning_rate=0.300000012, max_delta_step=0, max_depth=6,
          min_child_weight=1, missing=nan, monotone_constraints='()',
          n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=45,
          reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
          tree_method='exact', validate_parameters=1, verbosity=None)

​

user_120970460
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