8e0a08fbcf
* a few tweaks to speed up data generation * del variable to save memory * switch to random numpy arrays
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
"""Run benchmark on the tree booster."""
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import argparse
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import ast
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import time
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import numpy as np
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import xgboost as xgb
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RNG = np.random.RandomState(1994)
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def run_benchmark(args):
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"""Runs the benchmark."""
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try:
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dtest = xgb.DMatrix('dtest.dm')
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dtrain = xgb.DMatrix('dtrain.dm')
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if not (dtest.num_col() == args.columns
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and dtrain.num_col() == args.columns):
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raise ValueError("Wrong cols")
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if not (dtest.num_row() == args.rows * args.test_size
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and dtrain.num_row() == args.rows * (1 - args.test_size)):
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raise ValueError("Wrong rows")
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except xgb.core.XGBoostError:
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print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
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print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
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tmp = time.time()
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X = RNG.rand(args.rows, args.columns)
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y = RNG.randint(0, 2, args.rows)
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if 0.0 < args.sparsity < 1.0:
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X = np.array([[np.nan if RNG.uniform(0, 1) < args.sparsity else x for x in x_row]
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for x_row in X])
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train_rows = int(args.rows * (1.0 - args.test_size))
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test_rows = int(args.rows * args.test_size)
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X_train = X[:train_rows, :]
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X_test = X[-test_rows:, :]
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y_train = y[:train_rows]
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y_test = y[-test_rows:]
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print("Generate Time: %s seconds" % (str(time.time() - tmp)))
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del X, y
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tmp = time.time()
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print("DMatrix Start")
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dtrain = xgb.DMatrix(X_train, y_train, nthread=-1)
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dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
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print("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
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del X_train, y_train, X_test, y_test
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dtest.save_binary('dtest.dm')
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dtrain.save_binary('dtrain.dm')
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param = {'objective': 'binary:logistic'}
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if args.params != '':
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param.update(ast.literal_eval(args.params))
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param['tree_method'] = args.tree_method
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print("Training with '%s'" % param['tree_method'])
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tmp = time.time()
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xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
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print("Train Time: %s seconds" % (str(time.time() - tmp)))
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def main():
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"""The main function.
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Defines and parses command line arguments and calls the benchmark.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('--tree_method', default='gpu_hist')
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parser.add_argument('--sparsity', type=float, default=0.0)
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parser.add_argument('--rows', type=int, default=1000000)
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parser.add_argument('--columns', type=int, default=50)
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parser.add_argument('--iterations', type=int, default=500)
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parser.add_argument('--test_size', type=float, default=0.25)
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parser.add_argument('--params', default='',
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help='Provide additional parameters as a Python dict string, e.g. --params '
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'\"{\'max_depth\':2}\"')
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args = parser.parse_args()
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run_benchmark(args)
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if __name__ == '__main__':
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main()
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