[python-package] Classifier fitted to non-binary data cannot be refit to binary data

[david-cortes]

If I instantiate an LGBMClassifier object and fit it to non-binary data, the object will error out when refitting to binary data:

import numpy as np
from lightgbm import LGBMClassifier

rng = np.random.default_rng(seed=123)
nrows = 1000
ncols = 20

X = rng.standard_normal(size=(nrows,ncols))
y_bin = (rng.random(size=nrows) <= .3).astype(np.float64)
y_multi = rng.integers(4, size=nrows)

model = LGBMClassifier()
model.fit(X, y_multi)
model.fit(X, y_bin)

---------------------------------------------------------------------------
LightGBMError                             Traceback (most recent call last)
Untitled-1 in <module>
----> <a href='untitled:Untitled-1?line=13'>14</a> model.fit(X, y_bin)

~/anaconda3/lib/python3.9/site-packages/lightgbm/sklearn.py in 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)
    965                     valid_sets[i] = (valid_x, self._le.transform(valid_y))
    966 
--> 967         super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets,
    968                     eval_names=eval_names, eval_sample_weight=eval_sample_weight,
    969                     eval_class_weight=eval_class_weight, eval_init_score=eval_init_score,

~/anaconda3/lib/python3.9/site-packages/lightgbm/sklearn.py in 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)
    746         callbacks.append(record_evaluation(evals_result))
    747 
--> 748         self._Booster = train(
    749             params=params,
    750             train_set=train_set,

~/anaconda3/lib/python3.9/site-packages/lightgbm/engine.py in train(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)
    269     # construct booster
    270     try:
--> 271         booster = Booster(params=params, train_set=train_set)
    272         if is_valid_contain_train:
    273             booster.set_train_data_name(train_data_name)
...
--> 125         raise LightGBMError(_LIB.LGBM_GetLastError().decode('utf-8'))
    126 
    127 

LightGBMError: Number of classes should be specified and greater than 1 for multiclass training

[github-actions]

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