# fitting the knn with train-test split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

 # Optimisation via gridSearch
 knn=KNeighborsClassifier()
 params={'n_neighbors': range(1,40), 'weights':['uniform', 'distance'], 'metric':['minkowski','euclidean'],'algorithm': ['auto','ball_tree','kd_tree', 'brute']}
 k_grd=GridSearchCV(estimator=knn,param_grid=params,cv=5)
 k_grd.fit(X_train,y_train)

 # testing
 yk_grd=k_grd.predict(X_test)

 # calculating the logloss
 print (log_loss(y_test, yk_grd))

y_true and y_pred contain different number of classes 93, 2. Please provide the true labels explicitly through the labels argument. Classes found in y_true.

X_train.shape, X_test.shape, y_train.shape, y_test.shape, yk_grd.shape
# results
((742, 192), (248, 192), (742,), (248,), (248,))