Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Refactor Python tests. (#3897)

Browse Source 
* Deprecate nose tests.
* Format python tests.
This commit is contained in:
Jiaming Yuan
2018-11-15 13:56:33 +13:00
committed by GitHub
parent c76d993681
commit 2ea0f887c1
23 changed files with 302 additions and 225 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
tests/python-gpu/test_gpu_prediction.py
View File

@@ -1,15 +1,14 @@
from __future__ import print_function
import numpy as np
import sys
import unittest
import xgboost as xgb
from nose.plugins.attrib import attr
import pytest
rng = np.random.RandomState(1994)
@attr('gpu')
@pytest.mark.gpu
class TestGPUPredict(unittest.TestCase):
def test_predict(self):
iterations = 10
@@ -18,9 +17,12 @@ class TestGPUPredict(unittest.TestCase):
test_num_cols = [10, 50, 500]
for num_rows in test_num_rows:
for num_cols in test_num_cols:
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))
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 = {
@@ -28,7 +30,8 @@ class TestGPUPredict(unittest.TestCase):
"predictor": "gpu_predictor",
'eval_metric': 'auc',
}
bst = xgb.train(param, dtrain, 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_train = bst.predict(dtrain, output_margin=True)
gpu_pred_test = bst.predict(dtest, output_margin=True)
@@ -39,21 +42,26 @@ class TestGPUPredict(unittest.TestCase):
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)
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
# 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.model_selection 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)
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)
@@ -85,8 +93,7 @@ class TestGPUPredict(unittest.TestCase):
params = {'tree_method': 'gpu_hist',
'predictor': 'cpu_predictor',
'n_jobs': -1,
'seed': 123
}
'seed': 123}
m = xgb.XGBRegressor(**params).fit(X_train, y_train)
cpu_train_score = m.score(X_train, y_train)
cpu_test_score = m.score(X_test, y_test)