Light GBM

import lightgbm as lgb
from sklearn import metrics

def auc2(m, train, test): 
    return (metrics.roc_auc_score(y_train,m.predict(train)),
                            metrics.roc_auc_score(y_test,m.predict(test)))

lg = lgb.LGBMClassifier(silent=False)
param_dist = {"max_depth": [25,50, 75],
              "learning_rate" : [0.01,0.05,0.1],
              "num_leaves": [300,900,1200],
              "n_estimators": [200]
             }
grid_search = GridSearchCV(lg, n_jobs=-1, param_grid=param_dist, cv = 3, scoring="roc_auc", verbose=5)
grid_search.fit(train,y_train)
grid_search.best_estimator_

d_train = lgb.Dataset(train, label=y_train)
params = {"max_depth": 50, "learning_rate" : 0.1, "num_leaves": 900,  "n_estimators": 300}

# Without Categorical Features
model2 = lgb.train(params, d_train)
auc2(model2, train, test)

#With Catgeorical Features
cate_features_name = ["MONTH","DAY","DAY_OF_WEEK","AIRLINE","DESTINATION_AIRPORT",
                 "ORIGIN_AIRPORT"]
model2 = lgb.train(params, d_train, categorical_feature = cate_features_name)
auc2(model2, train, test)

You need add 'free_raw_data=False' in lgb.Dataset
d_train = lgb.Dataset(train, label=y_train, free_raw_data=False)