8b04736b81
* Add inplace prediction for dask-cudf. * Remove Dockerfile.release, since it's not used anywhere * Use Conda exclusively in CUDF and GPU containers * Improve cupy memory copying. * Add skip marks to tests. * Add mgpu-cudf category on the CI to run all distributed tests. Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
'''Tests for running inplace prediction.'''
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import unittest
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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from scipy import sparse
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import xgboost as xgb
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def run_threaded_predict(X, rows, predict_func):
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results = []
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per_thread = 20
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with ThreadPoolExecutor(max_workers=10) as e:
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for i in range(0, rows, int(rows / per_thread)):
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if hasattr(X, 'iloc'):
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predictor = X.iloc[i:i+per_thread, :]
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else:
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predictor = X[i:i+per_thread, ...]
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f = e.submit(predict_func, predictor)
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results.append(f)
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for f in results:
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assert f.result()
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class TestInplacePredict(unittest.TestCase):
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'''Tests for running inplace prediction'''
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def test_predict(self):
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rows = 1000
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cols = 10
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np.random.seed(1994)
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X = np.random.randn(rows, cols)
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y = np.random.randn(rows)
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dtrain = xgb.DMatrix(X, y)
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booster = xgb.train({'tree_method': 'hist'},
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dtrain, num_boost_round=10)
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test = xgb.DMatrix(X[:10, ...])
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predt_from_array = booster.inplace_predict(X[:10, ...])
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predt_from_dmatrix = booster.predict(test)
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np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)
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def predict_dense(x):
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inplace_predt = booster.inplace_predict(x)
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d = xgb.DMatrix(x)
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copied_predt = booster.predict(d)
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return np.all(copied_predt == inplace_predt)
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for i in range(10):
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run_threaded_predict(X, rows, predict_dense)
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def predict_csr(x):
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inplace_predt = booster.inplace_predict(sparse.csr_matrix(x))
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d = xgb.DMatrix(x)
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copied_predt = booster.predict(d)
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return np.all(copied_predt == inplace_predt)
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for i in range(10):
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run_threaded_predict(X, rows, predict_csr)
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