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

-Add experimental GPU algorithm for lossguided mode (#2755)

Browse Source 
-Improved GPU algorithm unit tests
-Removed some thrust code to improve compile times
This commit is contained in:
Rory Mitchell
2017-10-01 00:18:35 +13:00
committed by GitHub
parent 69c3b78a29
commit 4cb2f7598b
14 changed files with 1291 additions and 593 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
tests/benchmark/benchmark.py
View File

@@ -5,57 +5,45 @@ import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import time
import ast
rng = np.random.RandomState(1994)
def run_benchmark(args, gpu_algorithm, cpu_algorithm):
def run_benchmark(args):
print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
tmp = time.time()
X, y = make_classification(args.rows, n_features=args.columns, random_state=7)
if args.sparsity < 1.0:
X = np.array([[np.nan if rng.uniform(0, 1) < args.sparsity else x for x in x_row] for x_row in X])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size, random_state=7)
print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
print ("DMatrix Start")
# omp way
dtrain = xgb.DMatrix(X_train, y_train, nthread=-1)
dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
param = {'objective': 'binary:logistic',
'max_depth': 6,
'silent': 0,
'n_gpus': 1,
'gpu_id': 0,
'eval_metric': 'error',
'debug_verbose': 0,
}
param = {'objective': 'binary:logistic'}
if args.params is not '':
param.update(ast.literal_eval(args.params))
param['tree_method'] = gpu_algorithm
param['tree_method'] = args.tree_method
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
param['silent'] = 1
param['tree_method'] = cpu_algorithm
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
print ("Time: %s seconds" % (str(time.time() - tmp)))
parser = argparse.ArgumentParser()
parser.add_argument('--algorithm', choices=['all', 'gpu_exact', 'gpu_hist'], default='all')
parser.add_argument('--tree_method', default='gpu_hist')
parser.add_argument('--sparsity', type=float, default=0.0)
parser.add_argument('--rows', type=int, default=1000000)
parser.add_argument('--columns', type=int, default=50)
parser.add_argument('--iterations', type=int, default=500)
parser.add_argument('--test_size', type=float, default=0.25)
parser.add_argument('--params', default='', help='Provide additional parameters as a Python dict string, e.g. --params \"{\'max_depth\':2}\"')
args = parser.parse_args()
if 'gpu_hist' in args.algorithm:
run_benchmark(args, args.algorithm, 'hist')
elif 'gpu_exact' in args.algorithm:
run_benchmark(args, args.algorithm, 'exact')
elif 'all' in args.algorithm:
run_benchmark(args, 'gpu_exact', 'exact')
run_benchmark(args, 'gpu_hist', 'hist')
run_benchmark(args)