8d1098a983
* In AUC and AUCPR metrics, detect whether weights are per-instance or per-group * Fix C++ style check * Add a test for weighted AUC
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
import numpy as np
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from scipy.sparse import csr_matrix
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import xgboost
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def test_ranking_with_unweighted_data():
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Xrow = np.array([1, 2, 6, 8, 11, 14, 16, 17])
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Xcol = np.array([0, 0, 1, 1, 2, 2, 3, 3])
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X = csr_matrix((np.ones(shape=8), (Xrow, Xcol)), shape=(20, 4))
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y = np.array([0.0, 1.0, 1.0, 0.0, 0.0,
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0.0, 1.0, 0.0, 1.0, 0.0,
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0.0, 1.0, 0.0, 0.0, 1.0,
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0.0, 1.0, 1.0, 0.0, 0.0])
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group = np.array([5, 5, 5, 5], dtype=np.uint)
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dtrain = xgboost.DMatrix(X, label=y)
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dtrain.set_group(group)
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params = {'eta': 1, 'tree_method': 'exact',
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'objective': 'rank:pairwise', 'eval_metric': ['auc', 'aucpr'],
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'max_depth': 1}
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evals_result = {}
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bst = xgboost.train(params, dtrain, 10, evals=[(dtrain, 'train')],
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evals_result=evals_result)
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auc_rec = evals_result['train']['auc']
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assert all(p <= q for p, q in zip(auc_rec, auc_rec[1:]))
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auc_rec = evals_result['train']['aucpr']
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assert all(p <= q for p, q in zip(auc_rec, auc_rec[1:]))
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def test_ranking_with_weighted_data():
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Xrow = np.array([1, 2, 6, 8, 11, 14, 16, 17])
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Xcol = np.array([0, 0, 1, 1, 2, 2, 3, 3])
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X = csr_matrix((np.ones(shape=8), (Xrow, Xcol)), shape=(20, 4))
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y = np.array([0.0, 1.0, 1.0, 0.0, 0.0,
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0.0, 1.0, 0.0, 1.0, 0.0,
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0.0, 1.0, 0.0, 0.0, 1.0,
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0.0, 1.0, 1.0, 0.0, 0.0])
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weights = np.array([1.0, 2.0, 3.0, 4.0])
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group = np.array([5, 5, 5, 5], dtype=np.uint)
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dtrain = xgboost.DMatrix(X, label=y, weight=weights)
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dtrain.set_group(group)
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params = {'eta': 1, 'tree_method': 'exact',
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'objective': 'rank:pairwise', 'eval_metric': ['auc', 'aucpr'],
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'max_depth': 1}
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evals_result = {}
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bst = xgboost.train(params, dtrain, 10, evals=[(dtrain, 'train')],
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evals_result=evals_result)
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auc_rec = evals_result['train']['auc']
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assert all(p <= q for p, q in zip(auc_rec, auc_rec[1:]))
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auc_rec = evals_result['train']['aucpr']
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assert all(p <= q for p, q in zip(auc_rec, auc_rec[1:]))
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for i in range(1, 11):
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pred = bst.predict(dtrain, ntree_limit=i)
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# is_sorted[i]: is i-th group correctly sorted by the ranking predictor?
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is_sorted = []
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for k in range(0, 20, 5):
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ind = np.argsort(-pred[k:k+5])
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z = y[ind+k]
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is_sorted.append(all(i >= j for i, j in zip(z, z[1:])))
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# Since we give weights 1, 2, 3, 4 to the four query groups,
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# the ranking predictor will first try to correctly sort the last query group
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# before correctly sorting other groups.
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assert all(p <= q for p, q in zip(is_sorted, is_sorted[1:]))
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