LightGBMError: Number of classes should be specified and greater than 1 for multiclass training about arfs HOT 1 CLOSED

from sklearn.datasets import load_wine
import pandas as pd
import arfs
from arfs.allrelevant import GrootCV
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

#loading the dataset
X1 = load_wine()
df_1 = pd.DataFrame(X1.data, columns=X1.feature_names)
Y_1 = X1.target

#Scaling using the Standard Scaler
sc_1 = StandardScaler()
sc_1.fit(df_1)
X_1 = pd.DataFrame(sc_1.fit_transform(df_1))

print(f'Testing with ARFS version {arfs.__version__}')
print(f"There are {len(np.unique(Y_1))} classes")

#train-test-split
X_train, X_test, y_train, y_test = train_test_split(X_1, Y_1, test_size=0.4, random_state=0)

feat_selector = GrootCV(objective='multiclass', cutoff=1, n_folds=5, n_iter=5, silent=True)
feat_selector.fit(X_train, y_train, sample_weight=None)

print(feat_selector.support_names_)
fig = feat_selector.plot_importance(n_feat_per_inch=5)