首先拿到的數(shù)據(jù)為樣本id 特征數(shù)據(jù) 以及 樣本id 和對應(yīng)的label兩個文檔
先把libsvm的特征處理成xgboost能夠使用的格式逆日，并按8:1:1的比例將數(shù)據(jù)分成訓(xùn)練集岩臣、驗證集以及測試集辣之。

#train id:0-42622
#valid id:42623-47957
#test id:47958-53285
def transdata(feature_file_path,group_file_path,out_feature_path_train,out_feature_path_vaild,out_feature_path_test):
    output_feature_train = open(out_feature_path_train,"w")
    output_feature_valid = open(out_feature_path_vaild, "w")
    output_feature_test = open(out_feature_path_test, "w")
    with open (feature_file_path) as features, open(group_file_path) as groups:
        for x,y in zip(features,groups):
            splits_x = x.strip().split("    ")
            splits_y = y.strip().split("    ")
            if int(splits_x[0])<42623:
                output_feature_train.write(splits_y[0]+" "+splits_x[1]+"\n")
            if int(splits_x[0])>42622 and int(splits_x[0])<47958:
                output_feature_valid.write(splits_y[0]+" "+splits_x[1]+"\n")
            if int(splits_x[0])>47957:
                output_feature_test.write(splits_y[0]+" "+splits_x[1]+"\n")
    output_feature_train.close()
    output_feature_valid.close()
    output_feature_test.close()

if __name__ =="__main__":
    transdata("topic_features.txt","gt.txt","libsvm_format.train.txt","libsvm_format.valid.txt","libsvm_format.test.txt")

對label進(jìn)行分組

#train id:0-42622
#valid id:42623-47957
#test id:47958-53285
def transdata(group_file_path,out_feature_path_train,out_feature_path_vaild,out_feature_path_test):
    output_feature_train = open(out_feature_path_train,"w")
    output_feature_valid = open(out_feature_path_vaild, "w")
    output_feature_test = open(out_feature_path_test, "w")
    groups = open(group_file_path)
    for line in groups:
        if not line:
            break
        splits_x = line.strip().split(" ")
        if int(splits_x[1]) < 42623:
            output_feature_train.write(line)
        if int(splits_x[1]) > 42622 and int(splits_x[0]) < 47958:
            output_feature_valid.write(line)
        if int(splits_x[1]) > 47957:
            output_feature_test.write(line)

    output_feature_train.close()
    output_feature_valid.close()
    output_feature_test.close()

if __name__ =="__main__":
    transdata("gt.txt","gt.train.txt","gt.valid.txt","gt.test.txt")

隨即根據(jù)qid對數(shù)據(jù)進(jìn)行分組，生成.group文件

#train id:0-42622
#valid id:42623-47957
#test id:47958-53285
def transgroup(group_file_path,save_path):
   group_output = open(save_path,"w")
   group_file = open(group_file_path)
   group = ""
   group_data = []
   for line in group_file:
       if not line:
           break
       splits = line.strip().split("   ")
       if splits[2]!=group:
           group_output.write(str(len(group_data))+"\n")
           group_data = []
       group = splits[2]
       group_data.append(splits[0])

   group_output.write(str(len(group_data)) + "\n")
   group_output.close()
   group_file.close()
if __name__ =="__main__":
   transgroup("gt.test.txt","group.test.txt")

objective參數(shù)的解釋
`rank:pairwise`: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
`rank:ndcg`: Use LambdaMART to perform list-wise ranking where [Normalized Discounted Cumulative Gain (NDCG)](http://en.wikipedia.org/wiki/NDCG) is maximized

數(shù)據(jù)處理完成后，送入xgboost進(jìn)行訓(xùn)練

#!/usr/bin/python
import xgboost as xgb
from xgboost import DMatrix
from sklearn.datasets import load_svmlight_file

#  This script demonstrate how to do ranking with xgboost.train
x_train, y_train = load_svmlight_file("libsvm_format.train.txt")
x_valid, y_valid = load_svmlight_file("libsvm_format.valid.txt")
x_test, y_test = load_svmlight_file("libsvm_format.test.txt")

group_train = []
with open("group.train.txt", "r") as f:
   data = f.readlines()
   for line in data:
       group_train.append(int(line.split("\n")[0]))

group_valid = []
with open("group.valid.txt", "r") as f:
   data = f.readlines()
   for line in data:
       group_valid.append(int(line.split("\n")[0]))

group_test = []
with open("group.test.txt", "r") as f:
   data = f.readlines()
   for line in data:
       group_test.append(int(line.split("\n")[0]))

train_dmatrix = DMatrix(x_train, y_train)
valid_dmatrix = DMatrix(x_valid, y_valid)
test_dmatrix = DMatrix(x_test)

train_dmatrix.set_group(group_train)
valid_dmatrix.set_group(group_valid)

params = {'objective': 'rank:pairwise', 'eta': 0.1, 'gamma': 1.0,
         'min_child_weight': 0.1, 'max_depth': 6}
xgb_model = xgb.train(params, train_dmatrix, num_boost_round=4,
                     evals=[(valid_dmatrix, 'validation')])
pred = xgb_model.predict(test_dmatrix)

參考代碼1：http://www.reibang.com/p/9caef967ec0a

參考代碼2: https://github.com/dmlc/xgboost/blob/master/demo/rank/rank.py