42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
#!/usr/bin/python
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import xgboost as xgb
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from xgboost import DMatrix
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from sklearn.datasets import load_svmlight_file
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# This script demonstrate how to do ranking with xgboost.train
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x_train, y_train = load_svmlight_file("mq2008.train")
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x_valid, y_valid = load_svmlight_file("mq2008.vali")
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x_test, y_test = load_svmlight_file("mq2008.test")
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group_train = []
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with open("mq2008.train.group", "r") as f:
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data = f.readlines()
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for line in data:
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group_train.append(int(line.split("\n")[0]))
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group_valid = []
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with open("mq2008.vali.group", "r") as f:
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data = f.readlines()
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for line in data:
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group_valid.append(int(line.split("\n")[0]))
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group_test = []
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with open("mq2008.test.group", "r") as f:
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data = f.readlines()
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for line in data:
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group_test.append(int(line.split("\n")[0]))
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train_dmatrix = DMatrix(x_train, y_train)
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valid_dmatrix = DMatrix(x_valid, y_valid)
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test_dmatrix = DMatrix(x_test)
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train_dmatrix.set_group(group_train)
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valid_dmatrix.set_group(group_valid)
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params = {'objective': 'rank:ndcg', 'eta': 0.1, 'gamma': 1.0,
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'min_child_weight': 0.1, 'max_depth': 6}
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xgb_model = xgb.train(params, train_dmatrix, num_boost_round=4,
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evals=[(valid_dmatrix, 'validation')])
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pred = xgb_model.predict(test_dmatrix)
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