2019-11-19：xgboost用于排序的算法

def train_pairwise(self):
    """ train and predict ranking model with pairwise mode """
    X, Y, indices, _ = self.prepare_data(root_dir+"/data/pairwise_data/train_features.txt.%s" % self.args.task_purpose)
    dtrain = xgb.DMatrix(X, label=Y)
    dtrain_group = self.load_group_file(root_dir+"/data/pairwise_data/train_features.txt.%s.group" % self.args.task_purpose)
    dtrain.set_group(dtrain_group)

    params = {"objective": "rank:pairwise",
              "min_child_weight": 1,
              "eta": 0.1,
              "gamma": 1.0,
              "max_depth": 20}

    watchlist = [(dtrain, 'train')]
    num_round = 5000
    self.bst = xgb.train(params, dtrain, num_round, watchlist)