1b77903eeb
* Fix #2905 * Fix gpu_exact test failures * Fix bug in GPU prediction where multiple calls to batch prediction can produce incorrect results * Fix GPU documentation formatting
75 lines
3.1 KiB
Python
75 lines
3.1 KiB
Python
from __future__ import print_function
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import numpy as np
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import sys
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import unittest
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import xgboost as xgb
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from nose.plugins.attrib import attr
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rng = np.random.RandomState(1994)
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@attr('gpu')
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class TestGPUPredict(unittest.TestCase):
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def test_predict(self):
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iterations = 10
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np.random.seed(1)
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test_num_rows = [10, 1000, 5000]
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test_num_cols = [10, 50, 500]
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for num_rows in test_num_rows:
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for num_cols in test_num_cols:
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dtrain = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
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dval = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
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dtest = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
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watchlist = [(dtrain, 'train'), (dval, 'validation')]
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res = {}
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param = {
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"objective": "binary:logistic",
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"predictor": "gpu_predictor",
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'eval_metric': 'auc',
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}
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bst = xgb.train(param, dtrain, iterations, evals=watchlist, evals_result=res)
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assert self.non_decreasing(res["train"]["auc"])
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gpu_pred_train = bst.predict(dtrain, output_margin=True)
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gpu_pred_test = bst.predict(dtest, output_margin=True)
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gpu_pred_val = bst.predict(dval, output_margin=True)
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param["predictor"] = "cpu_predictor"
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bst_cpu = xgb.train(param, dtrain, iterations, evals=watchlist)
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cpu_pred_train = bst_cpu.predict(dtrain, output_margin=True)
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cpu_pred_test = bst_cpu.predict(dtest, output_margin=True)
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cpu_pred_val = bst_cpu.predict(dval, output_margin=True)
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np.testing.assert_allclose(cpu_pred_train, gpu_pred_train, rtol=1e-5)
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np.testing.assert_allclose(cpu_pred_val, gpu_pred_val, rtol=1e-5)
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np.testing.assert_allclose(cpu_pred_test, gpu_pred_test, rtol=1e-5)
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def non_decreasing(self, L):
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return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
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# Test case for a bug where multiple batch predictions made on a test set produce incorrect results
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def test_multi_predict(self):
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from sklearn.datasets import make_regression
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from sklearn.cross_validation import train_test_split
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n = 1000
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X, y = make_regression(n, random_state=rng)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)
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dtrain = xgb.DMatrix(X_train, label=y_train)
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dtest = xgb.DMatrix(X_test)
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params = {}
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params["tree_method"] = "gpu_hist"
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params['predictor'] = "gpu_predictor"
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bst_gpu_predict = xgb.train(params, dtrain)
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params['predictor'] = "cpu_predictor"
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bst_cpu_predict = xgb.train(params, dtrain)
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predict0 = bst_gpu_predict.predict(dtest)
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predict1 = bst_gpu_predict.predict(dtest)
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cpu_predict = bst_cpu_predict.predict(dtest)
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assert np.allclose(predict0, predict1)
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assert np.allclose(predict0, cpu_predict)
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