def test_fast_histmaker(self):
variable_param = {'tree_method': ['hist'],
'max_depth': [2, 8],
'max_bin': [2, 256],
'grow_policy': ['depthwise', 'lossguide'],
'max_leaves': [64, 0],
'verbosity': [0]}
for param in parameter_combinations(variable_param):
result = run_suite(param)
assert_results_non_increasing(result, 1e-2)
# hist must be same as exact on all-categorial data
dpath = HOME_DIR + 'demo/data/'
ag_dtrain = xgb.DMatrix({username: dpath + 'agaricus.txt.train.enc'})
ag_dtest = xgb.DMatrix({username: dpath + 'agaricus.txt.test.enc'})
ag_param = {'max_depth': 2,
'tree_method': 'hist',
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic',
'eval_metric': 'auc'}
hist_res = {}
exact_res = {}
#TODO(rishabh): support for evals_result
"""
def test_fast_histmaker(self):
variable_param = {'tree_method': ['hist'],
'max_depth': [2, 8],
'max_bin': [2, 256],
'grow_policy': ['depthwise', 'lossguide'],
'max_leaves': [64, 0],
'verbosity': [0]}
for param in parameter_combinations(variable_param):
result = run_suite(param)
assert_results_non_increasing(result, 1e-2)
# hist must be same as exact on all-categorial data
dpath = 'demo/data/'
ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
ag_param = {'max_depth': 2,
'tree_method': 'hist',
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic',
'eval_metric': 'auc'}
hist_res = {}
exact_res = {}
xgb.train(ag_param, ag_dtrain, 10,
[(ag_dtrain, 'train'), (ag_dtest, 'test')],
evals_result=hist_res)
ag_param["tree_method"] = "exact"
xgb.train(ag_param, ag_dtrain, 10,
[(ag_dtrain, 'train'), (ag_dtest, 'test')],
evals_result=exact_res)
assert hist_res['train']['auc'] == exact_res['train']['auc']
assert hist_res['test']['auc'] == exact_res['test']['auc']
def test_gpu_hist_mgpu(self):
variable_param = {'n_gpus': [-1], 'max_depth': [2, 10],
'max_leaves': [255, 4],
'max_bin': [2, 256],
'grow_policy': ['lossguide']}
for param in parameter_combinations(variable_param):
param['tree_method'] = 'gpu_hist'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
def test_gpu_exact(self):
variable_param = {'max_depth': [2, 6, 15], }
for param in parameter_combinations(variable_param):
param['tree_method'] = 'gpu_exact'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
param['tree_method'] = 'exact'
cpu_results = run_suite(param, select_datasets=datasets)
assert_gpu_results(cpu_results, gpu_results)
def test_gpu_hist_mgpu(self):
variable_param = {'n_gpus': [-1], 'max_depth': [2, 10],
'max_leaves': [255, 4],
'max_bin': [2, 256],
'grow_policy': ['lossguide']}
for param in parameter_combinations(variable_param):
param['tree_method'] = 'gpu_hist'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
def test_shotgun(self):
variable_param = {'booster': ['gblinear'], 'updater':
['shotgun'], 'eta': [0.5], 'top_k': [10],
'tolerance': [1e-5], 'nthread': [2],
'alpha': [.005, .1], 'lambda': [.005],
'feature_selector': ['cyclic', 'shuffle']}
for param in parameter_combinations(variable_param):
results = run_suite(param, 200, self.datasets, True)
assert_regression_result(results, 1e-2)
assert_classification_result(results)
def test_specified_gpu_id_gpu_update(self):
variable_param = {'gpu_id': [1],
'max_depth': [8],
'max_leaves': [255, 4],
'max_bin': [2, 64],
'grow_policy': ['lossguide'],
'tree_method': ['gpu_hist']}
for param in parameter_combinations(variable_param):
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
def test_shotgun(self):
tm._skip_if_no_sklearn()
variable_param = {'booster': ['gblinear'], 'updater': ['shotgun'], 'eta': [0.5],
'top_k': [10], 'tolerance': [1e-5], 'nthread': [2],
'alpha': [.005, .1], 'lambda': [.005],
'feature_selector': ['cyclic', 'shuffle']
}
for param in parameter_combinations(variable_param):
results = run_suite(param, 200, None, True)
assert_regression_result(results, 1e-2)
assert_classification_result(results)
def test_specified_gpu_id_gpu_update(self):
variable_param = {'n_gpus': [1],
'gpu_id': [1],
'max_depth': [8],
'max_leaves': [255, 4],
'max_bin': [2, 64],
'grow_policy': ['lossguide'],
'tree_method': ['gpu_hist', 'gpu_exact']}
for param in parameter_combinations(variable_param):
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
def test_gpu_hist(self):
variable_param = {'n_gpus': [-1], 'max_depth': [2, 8],
'max_leaves': [255, 4],
'max_bin': [2, 256], 'min_child_weight': [0, 1],
'lambda': [0.0, 1.0],
'grow_policy': ['lossguide']}
for param in parameter_combinations(variable_param):
param['tree_method'] = 'gpu_hist'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
param['tree_method'] = 'hist'
cpu_results = run_suite(param, select_datasets=datasets)
assert_gpu_results(cpu_results, gpu_results)
def test_gpu_hist(self):
test_param = parameter_combinations({'n_gpus': [1], 'max_depth': [2, 8],
'max_leaves': [255, 4],
'max_bin': [2, 256],
'grow_policy': ['lossguide']})
test_param.append({'single_precision_histogram': True})
test_param.append({'min_child_weight': 0,
'lambda': 0})
for param in test_param:
param['tree_method'] = 'gpu_hist'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
param['tree_method'] = 'hist'
cpu_results = run_suite(param, select_datasets=datasets)
assert_gpu_results(cpu_results, gpu_results)
def test_gpu_hist(self):
test_param = parameter_combinations({'n_gpus': [1], 'max_depth': [2, 8],
'max_leaves': [255, 4],
'max_bin': [2, 256],
'grow_policy': ['lossguide']})
test_param.append({'single_precision_histogram': True})
test_param.append({'min_child_weight': 0,
'lambda': 0})
for param in test_param:
param['tree_method'] = 'gpu_hist'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
param['tree_method'] = 'hist'
cpu_results = run_suite(param, select_datasets=datasets)
assert_gpu_results(cpu_results, gpu_results)
def test_coordinate(self):
variable_param = {
'booster': ['gblinear'],
'updater': ['coord_descent'],
'eta': [0.5],
'top_k': [10],
'tolerance': [1e-5],
'nthread': [2],
'alpha': [.005, .1],
'lambda': [.005],
'feature_selector': ['cyclic', 'shuffle', 'greedy', 'thrifty']
}
for param in parameter_combinations(variable_param):
results = run_suite(param, 150, self.datasets, scale_features=True)
assert_regression_result(results, 1e-2)
assert_classification_result(results)
def test_colmaker(self):
variable_param = {'updater': ['grow_colmaker'], 'max_depth': [2, 8]}
for param in parameter_combinations(variable_param):
result = run_suite(param)
assert_results_non_increasing(result, 1e-2)
def test_colmaker(self):
variable_param = {'updater': ['grow_colmaker'], 'max_depth': [2, 8]}
for param in parameter_combinations(variable_param):
result = run_suite(param)
assert_results_non_increasing(result, 1e-2)
def test_histmaker(self):
tm._skip_if_no_sklearn()
variable_param = {'updater': ['grow_histmaker'], 'max_depth': [2, 8]}
for param in parameter_combinations(variable_param):
result = run_suite(param)
assert_results_non_increasing(result, 1e-2)
for cpu_res, gpu_res in zip(cpu_results, gpu_results):
# Check final eval result roughly equivalent
assert np.allclose(cpu_res["eval"][-1], gpu_res["eval"][-1], 1e-2,
1e-2)
datasets = [
"Boston", "Cancer", "Digits", "Sparse regression",
"Sparse regression with weights", "Small weights regression"
]
test_param = parameter_combinations({
'gpu_id': [0],
'max_depth': [2, 8],
'max_leaves': [255, 4],
'max_bin': [2, 256],
'grow_policy': ['lossguide'],
'single_precision_histogram': [True],
'min_child_weight': [0],
'lambda': [0]
})
class TestGPU(unittest.TestCase):
def test_gpu_hist(self):
for param in test_param:
param['tree_method'] = 'gpu_hist'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
param['tree_method'] = 'hist'
cpu_results = run_suite(param, select_datasets=datasets)
assert_gpu_results(cpu_results, gpu_results)
def test_histmaker(self):
tm._skip_if_no_sklearn()
variable_param = {'updater': ['grow_histmaker'], 'max_depth': [2, 8]}
for param in parameter_combinations(variable_param):
result = run_suite(param)
assert_results_non_increasing(result, 1e-2)
