693ddb860e
* [CORE] DMatrix from sparse w/ explicit #col #row; safer arg types * [python-package] c-api change for _init_from_csr _init_from_csc * fix spaces * [R-package] adopt the new XGDMatrixCreateFromCSCEx interface * [CORE] redirect old sparse creators to new ones
34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
import numpy as np
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import xgboost as xgb
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from scipy.sparse import rand
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rng = np.random.RandomState(1)
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param = {'max_depth': 3, 'objective': 'binary:logistic', 'silent': 1}
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def test_sparse_dmatrix_csr():
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nrow = 100
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ncol = 1000
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x = rand(nrow, ncol, density=0.0005, format='csr', random_state=rng)
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assert x.indices.max() < ncol - 1
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x.data[:] = 1
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dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
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assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
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watchlist = [(dtrain, 'train')]
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bst = xgb.train(param, dtrain, 5, watchlist)
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bst.predict(dtrain)
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def test_sparse_dmatrix_csc():
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nrow = 1000
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ncol = 100
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x = rand(nrow, ncol, density=0.0005, format='csc', random_state=rng)
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assert x.indices.max() < nrow - 1
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x.data[:] = 1
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dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
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assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
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watchlist = [(dtrain, 'train')]
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bst = xgb.train(param, dtrain, 5, watchlist)
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bst.predict(dtrain)
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