8d06878bf9
* Use pre-rounding based method to obtain reproducible floating point summation. * GPU Hist for regression and classification are bit-by-bit reproducible. * Add doc. * Switch to thrust reduce for `node_sum_gradient`.
47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
import sys
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import os
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import unittest
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import numpy as np
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import xgboost as xgb
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sys.path.append("tests/python")
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# Don't import the test class, otherwise they will run twice.
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import test_basic_models as test_bm # noqa
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rng = np.random.RandomState(1994)
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class TestGPUBasicModels(unittest.TestCase):
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cputest = test_bm.TestModels()
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def test_eta_decay_gpu_hist(self):
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self.cputest.run_eta_decay('gpu_hist')
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def test_deterministic_gpu_hist(self):
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kRows = 1000
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kCols = 64
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kClasses = 4
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# Create large values to force rounding.
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X = np.random.randn(kRows, kCols) * 1e4
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y = np.random.randint(0, kClasses, size=kRows)
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cls = xgb.XGBClassifier(tree_method='gpu_hist',
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deterministic_histogram=True,
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single_precision_histogram=True)
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cls.fit(X, y)
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cls.get_booster().save_model('test_deterministic_gpu_hist-0.json')
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cls = xgb.XGBClassifier(tree_method='gpu_hist',
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deterministic_histogram=True,
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single_precision_histogram=True)
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cls.fit(X, y)
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cls.get_booster().save_model('test_deterministic_gpu_hist-1.json')
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with open('test_deterministic_gpu_hist-0.json', 'r') as fd:
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model_0 = fd.read()
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with open('test_deterministic_gpu_hist-1.json', 'r') as fd:
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model_1 = fd.read()
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assert hash(model_0) == hash(model_1)
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os.remove('test_deterministic_gpu_hist-0.json')
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os.remove('test_deterministic_gpu_hist-1.json')
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