I create a random dataset to train a LGBM model:
from sklearn.datasets import make_classificationX, y = make_classification()Then I train and predict the original LGBM model with no issues:
from lightgbm import LGBMClassifierclf = LGBMClassifier()clf.fit(X, y=y)clf.predict(X)clf.predict_proba(X)But when I create a custom class of LGBMClassifier, I get an error:
class MyClf(LGBMClassifier): def __init__(self, **kwargs): super().__init__(**kwargs) def fit(self, X, y=None): return super().fit(X, y=y) def predict(self, X): return super().predict(X) def predict_proba(self, X): return super().predict_proba(X)clf = MyClf()clf.fit(X, y=y)clf.predict(X)clf.predict_proba(X)In clf.fit:
---------------------------------------------------------------------------KeyError Traceback (most recent call last)Cell In[15], line 15 12 return super().predict_proba(X) 14 clf = MyClf()---> 15 clf.fit(X, y=y) 16 clf.predict(X) 17 clf.predict_proba(X)Cell In[15], line 6 5 def fit(self, X, y=None):----> 6 return super().fit(X, y=y)File lib/python3.9/site-packages/lightgbm/sklearn.py:890, in LGBMClassifier.fit(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks, init_model) 887 else: 888 valid_sets[i] = (valid_x, self._le.transform(valid_y))--> 890 super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets, 891 eval_names=eval_names, eval_sample_weight=eval_sample_weight, 892 eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, 893 eval_metric=eval_metric, early_stopping_rounds=early_stopping_rounds, 894 verbose=verbose, feature_name=feature_name, categorical_feature=categorical_feature, 895 callbacks=callbacks, init_model=init_model) 896 return selfFile lib/python3.9/site-packages/lightgbm/sklearn.py:570, in LGBMModel.fit(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks, init_model) 568 params.pop('n_estimators', None) 569 params.pop('class_weight', None)--> 570 if isinstance(params['random_state'], np.random.RandomState): 571 params['random_state'] = params['random_state'].randint(np.iinfo(np.int32).max) 572 for alias in _ConfigAliases.get('objective'):KeyError: 'random_state'I couldn't find the issue even I have inspected the source code of LGBMClassifier.
