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Fix several GPU bugs (#2916)

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* 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
This commit is contained in:
Rory Mitchell
2017-12-04 08:27:49 +13:00
committed by GitHub
parent 1e3aabbadc
commit 1b77903eeb
7 changed files with 109 additions and 46 deletions
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55
tests/python-gpu/test_gpu_prediction.py
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@@ -1,35 +1,74 @@
from __future__ import print_function
import numpy as np
import sys
import unittest
import xgboost as xgb
from nose.plugins.attrib import attr
rng = np.random.RandomState(1994)
@attr('gpu')
class TestGPUPredict(unittest.TestCase):
def test_predict(self):
iterations = 1
iterations = 10
np.random.seed(1)
test_num_rows = [10, 1000, 5000]
test_num_cols = [10, 50, 500]
for num_rows in test_num_rows:
for num_cols in test_num_cols:
dm = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
watchlist = [(dm, 'train')]
dtrain = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
dval = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
dtest = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
watchlist = [(dtrain, 'train'), (dval, 'validation')]
res = {}
param = {
"objective": "binary:logistic",
"predictor": "gpu_predictor",
'eval_metric': 'auc',
}
bst = xgb.train(param, dm, iterations, evals=watchlist, evals_result=res)
bst = xgb.train(param, dtrain, iterations, evals=watchlist, evals_result=res)
assert self.non_decreasing(res["train"]["auc"])
gpu_pred = bst.predict(dm, output_margin=True)
bst.set_param({"predictor": "cpu_predictor"})
cpu_pred = bst.predict(dm, output_margin=True)
np.testing.assert_allclose(cpu_pred, gpu_pred, rtol=1e-5)
gpu_pred_train = bst.predict(dtrain, output_margin=True)
gpu_pred_test = bst.predict(dtest, output_margin=True)
gpu_pred_val = bst.predict(dval, output_margin=True)
param["predictor"] = "cpu_predictor"
bst_cpu = xgb.train(param, dtrain, iterations, evals=watchlist)
cpu_pred_train = bst_cpu.predict(dtrain, output_margin=True)
cpu_pred_test = bst_cpu.predict(dtest, output_margin=True)
cpu_pred_val = bst_cpu.predict(dval, output_margin=True)
np.testing.assert_allclose(cpu_pred_train, gpu_pred_train, rtol=1e-5)
np.testing.assert_allclose(cpu_pred_val, gpu_pred_val, rtol=1e-5)
np.testing.assert_allclose(cpu_pred_test, gpu_pred_test, rtol=1e-5)
def non_decreasing(self, L):
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
# Test case for a bug where multiple batch predictions made on a test set produce incorrect results
def test_multi_predict(self):
from sklearn.datasets import make_regression
from sklearn.cross_validation import train_test_split
n = 1000
X, y = make_regression(n, random_state=rng)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test)
params = {}
params["tree_method"] = "gpu_hist"
params['predictor'] = "gpu_predictor"
bst_gpu_predict = xgb.train(params, dtrain)
params['predictor'] = "cpu_predictor"
bst_cpu_predict = xgb.train(params, dtrain)
predict0 = bst_gpu_predict.predict(dtest)
predict1 = bst_gpu_predict.predict(dtest)
cpu_predict = bst_cpu_predict.predict(dtest)
assert np.allclose(predict0, predict1)
assert np.allclose(predict0, cpu_predict)